1985 lines
89 KiB
Python
1985 lines
89 KiB
Python
"""
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Base class for all Zen MCP tools
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This module provides the abstract base class that all tools must inherit from.
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It defines the contract that tools must implement and provides common functionality
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for request validation, error handling, and response formatting.
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Key responsibilities:
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- Define the tool interface (abstract methods that must be implemented)
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- Handle request validation and file path security
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- Manage Gemini model creation with appropriate configurations
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- Standardize response formatting and error handling
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- Support for clarification requests when more information is needed
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"""
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import json
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import logging
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import os
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any, Literal, Optional
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from mcp.types import TextContent
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from pydantic import BaseModel, Field
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if TYPE_CHECKING:
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from tools.models import ToolModelCategory
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from config import MCP_PROMPT_SIZE_LIMIT
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from providers import ModelProvider, ModelProviderRegistry
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from providers.base import ProviderType
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from utils import check_token_limit
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from utils.conversation_memory import (
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MAX_CONVERSATION_TURNS,
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ConversationTurn,
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add_turn,
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create_thread,
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get_conversation_file_list,
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get_thread,
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)
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from utils.file_utils import read_file_content, read_files
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from .models import SPECIAL_STATUS_MODELS, ContinuationOffer, ToolOutput
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logger = logging.getLogger(__name__)
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class ToolRequest(BaseModel):
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"""
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Base request model for all tools.
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This Pydantic model defines common parameters that can be used by any tool.
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Tools can extend this model to add their specific parameters while inheriting
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these common fields.
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"""
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model: Optional[str] = Field(
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None,
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description="Model to use. See tool's input schema for available models and their capabilities.",
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)
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temperature: Optional[float] = Field(None, description="Temperature for response (tool-specific defaults)")
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# Thinking mode controls how much computational budget the model uses for reasoning
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# Higher values allow for more complex reasoning but increase latency and cost
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thinking_mode: Optional[Literal["minimal", "low", "medium", "high", "max"]] = Field(
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None,
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description=(
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"Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)"
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),
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)
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use_websearch: Optional[bool] = Field(
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True,
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description=(
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"Enable web search for documentation, best practices, and current information. "
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"When enabled, the model can request Claude to perform web searches and share results back "
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"during conversations. Particularly useful for: brainstorming sessions, architectural design "
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"discussions, exploring industry best practices, working with specific frameworks/technologies, "
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"researching solutions to complex problems, or when current documentation and community insights "
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"would enhance the analysis."
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),
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)
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continuation_id: Optional[str] = Field(
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None,
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description=(
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"Thread continuation ID for multi-turn conversations. When provided, the complete conversation "
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"history is automatically embedded as context. Your response should build upon this history "
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"without repeating previous analysis or instructions. Focus on providing only new insights, "
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"additional findings, or answers to follow-up questions. Can be used across different tools."
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),
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)
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images: Optional[list[str]] = Field(
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None,
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description=(
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"Optional image(s) for visual context. Accepts absolute file paths (must be FULL absolute paths to real files / folders - DO NOT SHORTEN) or "
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"base64 data URLs. Only provide when user explicitly mentions images. "
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"When including images, please describe what you believe each image contains "
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"(e.g., 'screenshot of error dialog', 'architecture diagram', 'code snippet') "
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"to aid with contextual understanding. Useful for UI discussions, diagrams, "
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"visual problems, error screens, architecture mockups, and visual analysis tasks."
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),
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)
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class BaseTool(ABC):
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# Class-level cache for OpenRouter registry to avoid multiple loads
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_openrouter_registry_cache = None
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"""
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Abstract base class for all Gemini tools.
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This class defines the interface that all tools must implement and provides
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common functionality for request handling, model creation, and response formatting.
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CONVERSATION-AWARE FILE PROCESSING:
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This base class implements the sophisticated dual prioritization strategy for
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conversation-aware file handling across all tools:
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1. FILE DEDUPLICATION WITH NEWEST-FIRST PRIORITY:
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- When same file appears in multiple conversation turns, newest reference wins
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- Prevents redundant file embedding while preserving most recent file state
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- Cross-tool file tracking ensures consistent behavior across analyze → codereview → debug
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2. CONVERSATION CONTEXT INTEGRATION:
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- All tools receive enhanced prompts with conversation history via reconstruct_thread_context()
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- File references from previous turns are preserved and accessible
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- Cross-tool knowledge transfer maintains full context without manual file re-specification
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3. TOKEN-AWARE FILE EMBEDDING:
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- Respects model-specific token allocation budgets from ModelContext
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- Prioritizes conversation history, then newest files, then remaining content
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- Graceful degradation when token limits are approached
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4. STATELESS-TO-STATEFUL BRIDGING:
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- Tools operate on stateless MCP requests but access full conversation state
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- Conversation memory automatically injected via continuation_id parameter
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- Enables natural AI-to-AI collaboration across tool boundaries
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To create a new tool:
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1. Create a new class that inherits from BaseTool
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2. Implement all abstract methods
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3. Define a request model that inherits from ToolRequest
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4. Register the tool in server.py's TOOLS dictionary
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"""
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# Class-level cache for OpenRouter registry to avoid repeated loading
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_openrouter_registry_cache = None
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@classmethod
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def _get_openrouter_registry(cls):
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"""Get cached OpenRouter registry instance, creating if needed."""
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# Use BaseTool class directly to ensure cache is shared across all subclasses
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if BaseTool._openrouter_registry_cache is None:
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from providers.openrouter_registry import OpenRouterModelRegistry
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BaseTool._openrouter_registry_cache = OpenRouterModelRegistry()
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logger.debug("Created cached OpenRouter registry instance")
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return BaseTool._openrouter_registry_cache
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def __init__(self):
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# Cache tool metadata at initialization to avoid repeated calls
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self.name = self.get_name()
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self.description = self.get_description()
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self.default_temperature = self.get_default_temperature()
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# Tool initialization complete
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@abstractmethod
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def get_name(self) -> str:
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"""
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Return the unique name identifier for this tool.
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This name is used by MCP clients to invoke the tool and must be
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unique across all registered tools.
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Returns:
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str: The tool's unique name (e.g., "review_code", "analyze")
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"""
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pass
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@abstractmethod
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def get_description(self) -> str:
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"""
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Return a detailed description of what this tool does.
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This description is shown to MCP clients (like Claude) to help them
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understand when and how to use the tool. It should be comprehensive
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and include trigger phrases.
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Returns:
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str: Detailed tool description with usage examples
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"""
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pass
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@abstractmethod
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def get_input_schema(self) -> dict[str, Any]:
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"""
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Return the JSON Schema that defines this tool's parameters.
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This schema is used by MCP clients to validate inputs before
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sending requests. It should match the tool's request model.
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Returns:
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Dict[str, Any]: JSON Schema object defining required and optional parameters
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"""
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pass
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@abstractmethod
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def get_system_prompt(self) -> str:
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"""
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Return the system prompt that configures the AI model's behavior.
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This prompt sets the context and instructions for how the model
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should approach the task. It's prepended to the user's request.
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Returns:
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str: System prompt with role definition and instructions
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"""
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pass
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@classmethod
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def _get_openrouter_registry(cls):
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"""Get cached OpenRouter registry instance."""
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if BaseTool._openrouter_registry_cache is None:
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import logging
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from providers.openrouter_registry import OpenRouterModelRegistry
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logger = logging.getLogger(__name__)
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logger.info("Loading OpenRouter registry for the first time (will be cached for all tools)")
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BaseTool._openrouter_registry_cache = OpenRouterModelRegistry()
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return BaseTool._openrouter_registry_cache
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def is_effective_auto_mode(self) -> bool:
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"""
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Check if we're in effective auto mode for schema generation.
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This determines whether the model parameter should be required in the tool schema.
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Used at initialization time when schemas are generated.
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Returns:
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bool: True if model parameter should be required in the schema
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"""
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from config import DEFAULT_MODEL
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from providers.registry import ModelProviderRegistry
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# Case 1: Explicit auto mode
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if DEFAULT_MODEL.lower() == "auto":
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return True
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# Case 2: Model not available (fallback to auto mode)
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if DEFAULT_MODEL.lower() != "auto":
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provider = ModelProviderRegistry.get_provider_for_model(DEFAULT_MODEL)
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if not provider:
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return True
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return False
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def _should_require_model_selection(self, model_name: str) -> bool:
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"""
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Check if we should require Claude to select a model at runtime.
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This is called during request execution to determine if we need
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to return an error asking Claude to provide a model parameter.
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Args:
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model_name: The model name from the request or DEFAULT_MODEL
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Returns:
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bool: True if we should require model selection
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"""
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# Case 1: Model is explicitly "auto"
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if model_name.lower() == "auto":
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return True
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# Case 2: Requested model is not available
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from providers.registry import ModelProviderRegistry
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provider = ModelProviderRegistry.get_provider_for_model(model_name)
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if not provider:
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logger = logging.getLogger(f"tools.{self.name}")
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logger.warning(f"Model '{model_name}' is not available with current API keys. Requiring model selection.")
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return True
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return False
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def _get_available_models(self) -> list[str]:
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"""
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Get list of all possible models for the schema enum.
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In auto mode, we show ALL models from MODEL_CAPABILITIES_DESC so Claude
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can see all options, even if some require additional API configuration.
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Runtime validation will handle whether a model is actually available.
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Returns:
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List of all model names from config
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"""
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from config import MODEL_CAPABILITIES_DESC
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# Start with all models from MODEL_CAPABILITIES_DESC
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all_models = list(MODEL_CAPABILITIES_DESC.keys())
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# Add OpenRouter models if OpenRouter is configured
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openrouter_key = os.getenv("OPENROUTER_API_KEY")
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if openrouter_key and openrouter_key != "your_openrouter_api_key_here":
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try:
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registry = self._get_openrouter_registry()
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# Add all aliases from the registry (includes OpenRouter cloud models)
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for alias in registry.list_aliases():
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if alias not in all_models:
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all_models.append(alias)
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except Exception as e:
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import logging
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logging.debug(f"Failed to add OpenRouter models to enum: {e}")
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# Add custom models if custom API is configured
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custom_url = os.getenv("CUSTOM_API_URL")
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if custom_url:
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try:
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registry = self._get_openrouter_registry()
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# Find all custom models (is_custom=true)
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for alias in registry.list_aliases():
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config = registry.resolve(alias)
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if config and hasattr(config, "is_custom") and config.is_custom:
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if alias not in all_models:
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all_models.append(alias)
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except Exception as e:
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import logging
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logging.debug(f"Failed to add custom models to enum: {e}")
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# Note: MODEL_CAPABILITIES_DESC already includes both short aliases (e.g., "flash", "o3")
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# and full model names (e.g., "gemini-2.5-flash") as keys
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# Remove duplicates while preserving order
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seen = set()
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unique_models = []
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for model in all_models:
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if model not in seen:
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seen.add(model)
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unique_models.append(model)
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return unique_models
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def get_model_field_schema(self) -> dict[str, Any]:
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"""
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Generate the model field schema based on auto mode configuration.
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When auto mode is enabled, the model parameter becomes required
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and includes detailed descriptions of each model's capabilities.
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Returns:
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Dict containing the model field JSON schema
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"""
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import os
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from config import DEFAULT_MODEL, MODEL_CAPABILITIES_DESC
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# Check if OpenRouter is configured
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has_openrouter = bool(
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os.getenv("OPENROUTER_API_KEY") and os.getenv("OPENROUTER_API_KEY") != "your_openrouter_api_key_here"
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)
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# Use the centralized effective auto mode check
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if self.is_effective_auto_mode():
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# In auto mode, model is required and we provide detailed descriptions
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model_desc_parts = [
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"IMPORTANT: Use the model specified by the user if provided, OR select the most suitable model "
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"for this specific task based on the requirements and capabilities listed below:"
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]
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for model, desc in MODEL_CAPABILITIES_DESC.items():
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model_desc_parts.append(f"- '{model}': {desc}")
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# Add custom models if custom API is configured
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custom_url = os.getenv("CUSTOM_API_URL")
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if custom_url:
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# Load custom models from registry
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try:
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registry = self._get_openrouter_registry()
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model_desc_parts.append(f"\nCustom models via {custom_url}:")
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# Find all custom models (is_custom=true)
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for alias in registry.list_aliases():
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config = registry.resolve(alias)
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if config and hasattr(config, "is_custom") and config.is_custom:
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# Format context window
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context_tokens = config.context_window
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if context_tokens >= 1_000_000:
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context_str = f"{context_tokens // 1_000_000}M"
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elif context_tokens >= 1_000:
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context_str = f"{context_tokens // 1_000}K"
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else:
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context_str = str(context_tokens)
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desc_line = f"- '{alias}' ({context_str} context): {config.description}"
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if desc_line not in model_desc_parts: # Avoid duplicates
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model_desc_parts.append(desc_line)
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except Exception as e:
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import logging
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logging.debug(f"Failed to load custom model descriptions: {e}")
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model_desc_parts.append(f"\nCustom models: Models available via {custom_url}")
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if has_openrouter:
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# Add OpenRouter models with descriptions
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try:
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import logging
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registry = self._get_openrouter_registry()
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# Group models by their model_name to avoid duplicates
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seen_models = set()
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model_configs = []
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for alias in registry.list_aliases():
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config = registry.resolve(alias)
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if config and config.model_name not in seen_models:
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seen_models.add(config.model_name)
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model_configs.append((alias, config))
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# Sort by context window (descending) then by alias
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model_configs.sort(key=lambda x: (-x[1].context_window, x[0]))
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if model_configs:
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model_desc_parts.append("\nOpenRouter models (use these aliases):")
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for alias, config in model_configs[:10]: # Limit to top 10
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# Format context window in human-readable form
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context_tokens = config.context_window
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if context_tokens >= 1_000_000:
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context_str = f"{context_tokens // 1_000_000}M"
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elif context_tokens >= 1_000:
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context_str = f"{context_tokens // 1_000}K"
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else:
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context_str = str(context_tokens)
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# Build description line
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if config.description:
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desc = f"- '{alias}' ({context_str} context): {config.description}"
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else:
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# Fallback to showing the model name if no description
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desc = f"- '{alias}' ({context_str} context): {config.model_name}"
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model_desc_parts.append(desc)
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# Add note about additional models if any were cut off
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total_models = len(model_configs)
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if total_models > 10:
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model_desc_parts.append(f"... and {total_models - 10} more models available")
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except Exception as e:
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# Log for debugging but don't fail
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import logging
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logging.debug(f"Failed to load OpenRouter model descriptions: {e}")
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# Fallback to simple message
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model_desc_parts.append(
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"\nOpenRouter models: If configured, you can also use ANY model available on OpenRouter."
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)
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# Get all available models for the enum
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all_models = self._get_available_models()
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return {
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"type": "string",
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"description": "\n".join(model_desc_parts),
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"enum": all_models,
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}
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else:
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# Normal mode - model is optional with default
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available_models = list(MODEL_CAPABILITIES_DESC.keys())
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models_str = ", ".join(f"'{m}'" for m in available_models)
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description = f"Model to use. Native models: {models_str}."
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if has_openrouter:
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# Add OpenRouter aliases
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try:
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registry = self._get_openrouter_registry()
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aliases = registry.list_aliases()
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# Show ALL aliases from the configuration
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if aliases:
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# Show all aliases so Claude knows every option available
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all_aliases = sorted(aliases)
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alias_list = ", ".join(f"'{a}'" for a in all_aliases)
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description += f" OpenRouter aliases: {alias_list}."
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else:
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description += " OpenRouter: Any model available on openrouter.ai."
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except Exception:
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description += (
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" OpenRouter: Any model available on openrouter.ai "
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"(e.g., 'gpt-4', 'claude-3-opus', 'mistral-large')."
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)
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description += f" Defaults to '{DEFAULT_MODEL}' if not specified."
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return {
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"type": "string",
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"description": description,
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}
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def get_default_temperature(self) -> float:
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"""
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Return the default temperature setting for this tool.
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Override this method to set tool-specific temperature defaults.
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Lower values (0.0-0.3) for analytical tasks, higher (0.7-1.0) for creative tasks.
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Returns:
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float: Default temperature between 0.0 and 1.0
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"""
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return 0.5
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def wants_line_numbers_by_default(self) -> bool:
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"""
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Return whether this tool wants line numbers added to code files by default.
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By default, ALL tools get line numbers for precise code references.
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Line numbers are essential for accurate communication about code locations.
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Line numbers add ~8-10% token overhead but provide precise targeting for:
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- Code review feedback ("SQL injection on line 45")
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- Debug error locations ("Memory leak in loop at lines 123-156")
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- Test generation targets ("Generate tests for method at lines 78-95")
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- Refactoring guidance ("Extract method from lines 67-89")
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- General code discussions ("Where is X defined?" -> "Line 42")
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The only exception is when reading diffs, which have their own line markers.
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Returns:
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bool: True if line numbers should be added by default for this tool
|
|
"""
|
|
return True # All tools get line numbers by default for consistency
|
|
|
|
def get_default_thinking_mode(self) -> str:
|
|
"""
|
|
Return the default thinking mode for this tool.
|
|
|
|
Thinking mode controls computational budget for reasoning.
|
|
Override for tools that need more or less reasoning depth.
|
|
|
|
Returns:
|
|
str: One of "minimal", "low", "medium", "high", "max"
|
|
"""
|
|
return "medium" # Default to medium thinking for better reasoning
|
|
|
|
def get_model_category(self) -> "ToolModelCategory":
|
|
"""
|
|
Return the model category for this tool.
|
|
|
|
Model category influences which model is selected in auto mode.
|
|
Override to specify whether your tool needs extended reasoning,
|
|
fast response, or balanced capabilities.
|
|
|
|
Returns:
|
|
ToolModelCategory: Category that influences model selection
|
|
"""
|
|
from tools.models import ToolModelCategory
|
|
|
|
return ToolModelCategory.BALANCED
|
|
|
|
def get_conversation_embedded_files(self, continuation_id: Optional[str]) -> list[str]:
|
|
"""
|
|
Get list of files already embedded in conversation history.
|
|
|
|
This method returns the list of files that have already been embedded
|
|
in the conversation history for a given continuation thread. Tools can
|
|
use this to avoid re-embedding files that are already available in the
|
|
conversation context.
|
|
|
|
Args:
|
|
continuation_id: Thread continuation ID, or None for new conversations
|
|
|
|
Returns:
|
|
list[str]: List of file paths already embedded in conversation history
|
|
"""
|
|
if not continuation_id:
|
|
# New conversation, no files embedded yet
|
|
return []
|
|
|
|
thread_context = get_thread(continuation_id)
|
|
if not thread_context:
|
|
# Thread not found, no files embedded
|
|
return []
|
|
|
|
embedded_files = get_conversation_file_list(thread_context)
|
|
logger.debug(f"[FILES] {self.name}: Found {len(embedded_files)} embedded files")
|
|
return embedded_files
|
|
|
|
def filter_new_files(self, requested_files: list[str], continuation_id: Optional[str]) -> list[str]:
|
|
"""
|
|
Filter out files that are already embedded in conversation history.
|
|
|
|
This method prevents duplicate file embeddings by filtering out files that have
|
|
already been embedded in the conversation history. This optimizes token usage
|
|
while ensuring tools still have logical access to all requested files through
|
|
conversation history references.
|
|
|
|
Args:
|
|
requested_files: List of files requested for current tool execution
|
|
continuation_id: Thread continuation ID, or None for new conversations
|
|
|
|
Returns:
|
|
list[str]: List of files that need to be embedded (not already in history)
|
|
"""
|
|
logger.debug(f"[FILES] {self.name}: Filtering {len(requested_files)} requested files")
|
|
|
|
if not continuation_id:
|
|
# New conversation, all files are new
|
|
logger.debug(f"[FILES] {self.name}: New conversation, all {len(requested_files)} files are new")
|
|
return requested_files
|
|
|
|
try:
|
|
embedded_files = set(self.get_conversation_embedded_files(continuation_id))
|
|
logger.debug(f"[FILES] {self.name}: Found {len(embedded_files)} embedded files in conversation")
|
|
|
|
# Safety check: If no files are marked as embedded but we have a continuation_id,
|
|
# this might indicate an issue with conversation history. Be conservative.
|
|
if not embedded_files:
|
|
logger.debug(f"{self.name} tool: No files found in conversation history for thread {continuation_id}")
|
|
logger.debug(
|
|
f"[FILES] {self.name}: No embedded files found, returning all {len(requested_files)} requested files"
|
|
)
|
|
return requested_files
|
|
|
|
# Return only files that haven't been embedded yet
|
|
new_files = [f for f in requested_files if f not in embedded_files]
|
|
logger.debug(
|
|
f"[FILES] {self.name}: After filtering: {len(new_files)} new files, {len(requested_files) - len(new_files)} already embedded"
|
|
)
|
|
logger.debug(f"[FILES] {self.name}: New files to embed: {new_files}")
|
|
|
|
# Log filtering results for debugging
|
|
if len(new_files) < len(requested_files):
|
|
skipped = [f for f in requested_files if f in embedded_files]
|
|
logger.debug(
|
|
f"{self.name} tool: Filtering {len(skipped)} files already in conversation history: {', '.join(skipped)}"
|
|
)
|
|
logger.debug(f"[FILES] {self.name}: Skipped (already embedded): {skipped}")
|
|
|
|
return new_files
|
|
|
|
except Exception as e:
|
|
# If there's any issue with conversation history lookup, be conservative
|
|
# and include all files rather than risk losing access to needed files
|
|
logger.warning(f"{self.name} tool: Error checking conversation history for {continuation_id}: {e}")
|
|
logger.warning(f"{self.name} tool: Including all requested files as fallback")
|
|
logger.debug(
|
|
f"[FILES] {self.name}: Exception in filter_new_files, returning all {len(requested_files)} files as fallback"
|
|
)
|
|
return requested_files
|
|
|
|
def format_conversation_turn(self, turn: ConversationTurn) -> list[str]:
|
|
"""
|
|
Format a conversation turn for display in conversation history.
|
|
|
|
Tools can override this to provide custom formatting for their responses
|
|
while maintaining the standard structure for cross-tool compatibility.
|
|
|
|
This method is called by build_conversation_history when reconstructing
|
|
conversation context, allowing each tool to control how its responses
|
|
appear in subsequent conversation turns.
|
|
|
|
Args:
|
|
turn: The conversation turn to format (from utils.conversation_memory)
|
|
|
|
Returns:
|
|
list[str]: Lines of formatted content for this turn
|
|
|
|
Example:
|
|
Default implementation returns:
|
|
["Files used in this turn: file1.py, file2.py", "", "Response content..."]
|
|
|
|
Tools can override to add custom sections, formatting, or metadata display.
|
|
"""
|
|
parts = []
|
|
|
|
# Add files context if present
|
|
if turn.files:
|
|
parts.append(f"Files used in this turn: {', '.join(turn.files)}")
|
|
parts.append("") # Empty line for readability
|
|
|
|
# Add the actual content
|
|
parts.append(turn.content)
|
|
|
|
return parts
|
|
|
|
def _prepare_file_content_for_prompt(
|
|
self,
|
|
request_files: list[str],
|
|
continuation_id: Optional[str],
|
|
context_description: str = "New files",
|
|
max_tokens: Optional[int] = None,
|
|
reserve_tokens: int = 1_000,
|
|
remaining_budget: Optional[int] = None,
|
|
arguments: Optional[dict] = None,
|
|
) -> tuple[str, list[str]]:
|
|
"""
|
|
Centralized file processing implementing dual prioritization strategy.
|
|
|
|
DUAL PRIORITIZATION STRATEGY CORE IMPLEMENTATION:
|
|
This method is the heart of conversation-aware file processing across all tools:
|
|
|
|
1. CONVERSATION-AWARE FILE DEDUPLICATION:
|
|
- Automatically detects and filters files already embedded in conversation history
|
|
- Implements newest-first prioritization: when same file appears in multiple turns,
|
|
only the newest reference is preserved to avoid redundant content
|
|
- Cross-tool file tracking ensures consistent behavior across tool boundaries
|
|
|
|
2. TOKEN-BUDGET OPTIMIZATION:
|
|
- Respects remaining token budget from conversation context reconstruction
|
|
- Prioritizes conversation history + newest file versions within constraints
|
|
- Graceful degradation when token limits approached (newest files preserved first)
|
|
- Model-specific token allocation ensures optimal context window utilization
|
|
|
|
3. CROSS-TOOL CONTINUATION SUPPORT:
|
|
- File references persist across different tools (analyze → codereview → debug)
|
|
- Previous tool file embeddings are tracked and excluded from new embeddings
|
|
- Maintains complete file context without manual re-specification
|
|
|
|
PROCESSING WORKFLOW:
|
|
1. Filter out files already embedded in conversation history using newest-first priority
|
|
2. Read content of only new files within remaining token budget
|
|
3. Generate informative notes about skipped files for user transparency
|
|
4. Return formatted content ready for prompt inclusion
|
|
|
|
Args:
|
|
request_files: List of files requested for current tool execution
|
|
continuation_id: Thread continuation ID, or None for new conversations
|
|
context_description: Description for token limit validation (e.g. "Code", "New files")
|
|
max_tokens: Maximum tokens to use (defaults to remaining budget or model-specific content allocation)
|
|
reserve_tokens: Tokens to reserve for additional prompt content (default 1K)
|
|
remaining_budget: Remaining token budget after conversation history (from server.py)
|
|
arguments: Original tool arguments (used to extract _remaining_tokens if available)
|
|
|
|
Returns:
|
|
tuple[str, list[str]]: (formatted_file_content, actually_processed_files)
|
|
- formatted_file_content: Formatted file content string ready for prompt inclusion
|
|
- actually_processed_files: List of individual file paths that were actually read and embedded
|
|
(directories are expanded to individual files)
|
|
"""
|
|
if not request_files:
|
|
return "", []
|
|
|
|
# Note: Even if conversation history is already embedded, we still need to process
|
|
# any NEW files that aren't in the conversation history yet. The filter_new_files
|
|
# method will correctly identify which files need to be embedded.
|
|
|
|
# Extract remaining budget from arguments if available
|
|
if remaining_budget is None:
|
|
# Use provided arguments or fall back to stored arguments from execute()
|
|
args_to_use = arguments or getattr(self, "_current_arguments", {})
|
|
remaining_budget = args_to_use.get("_remaining_tokens")
|
|
|
|
# Use remaining budget if provided, otherwise fall back to max_tokens or model-specific default
|
|
if remaining_budget is not None:
|
|
effective_max_tokens = remaining_budget - reserve_tokens
|
|
elif max_tokens is not None:
|
|
effective_max_tokens = max_tokens - reserve_tokens
|
|
else:
|
|
# The execute() method is responsible for setting self._model_context.
|
|
# A missing context is a programming error, not a fallback case.
|
|
if not hasattr(self, "_model_context") or not self._model_context:
|
|
logger.error(
|
|
f"[FILES] {self.name}: _prepare_file_content_for_prompt called without a valid model context. "
|
|
"This indicates an incorrect call sequence in the tool's implementation."
|
|
)
|
|
# Fail fast to reveal integration issues. A silent fallback with arbitrary
|
|
# limits can hide bugs and lead to unexpected token usage or silent failures.
|
|
raise RuntimeError("ModelContext not initialized before file preparation.")
|
|
|
|
# This is now the single source of truth for token allocation.
|
|
model_context = self._model_context
|
|
try:
|
|
token_allocation = model_context.calculate_token_allocation()
|
|
# Standardize on `file_tokens` for consistency and correctness.
|
|
# This fixes the bug where the old code incorrectly used content_tokens
|
|
effective_max_tokens = token_allocation.file_tokens - reserve_tokens
|
|
logger.debug(
|
|
f"[FILES] {self.name}: Using model context for {model_context.model_name}: "
|
|
f"{token_allocation.file_tokens:,} file tokens from {token_allocation.total_tokens:,} total"
|
|
)
|
|
except Exception as e:
|
|
logger.error(
|
|
f"[FILES] {self.name}: Failed to calculate token allocation from model context: {e}", exc_info=True
|
|
)
|
|
# If the context exists but calculation fails, we still need to prevent a crash.
|
|
# A loud error is logged, and we fall back to a safe default.
|
|
effective_max_tokens = 100_000 - reserve_tokens
|
|
|
|
# Ensure we have a reasonable minimum budget
|
|
effective_max_tokens = max(1000, effective_max_tokens)
|
|
|
|
files_to_embed = self.filter_new_files(request_files, continuation_id)
|
|
logger.debug(f"[FILES] {self.name}: Will embed {len(files_to_embed)} files after filtering")
|
|
|
|
# Log the specific files for debugging/testing
|
|
if files_to_embed:
|
|
logger.info(
|
|
f"[FILE_PROCESSING] {self.name} tool will embed new files: {', '.join([os.path.basename(f) for f in files_to_embed])}"
|
|
)
|
|
else:
|
|
logger.info(
|
|
f"[FILE_PROCESSING] {self.name} tool: No new files to embed (all files already in conversation history)"
|
|
)
|
|
|
|
content_parts = []
|
|
actually_processed_files = []
|
|
|
|
# Read content of new files only
|
|
if files_to_embed:
|
|
logger.debug(f"{self.name} tool embedding {len(files_to_embed)} new files: {', '.join(files_to_embed)}")
|
|
logger.debug(
|
|
f"[FILES] {self.name}: Starting file embedding with token budget {effective_max_tokens + reserve_tokens:,}"
|
|
)
|
|
try:
|
|
# Before calling read_files, expand directories to get individual file paths
|
|
from utils.file_utils import expand_paths
|
|
|
|
expanded_files = expand_paths(files_to_embed)
|
|
logger.debug(
|
|
f"[FILES] {self.name}: Expanded {len(files_to_embed)} paths to {len(expanded_files)} individual files"
|
|
)
|
|
|
|
file_content = read_files(
|
|
files_to_embed,
|
|
max_tokens=effective_max_tokens + reserve_tokens,
|
|
reserve_tokens=reserve_tokens,
|
|
include_line_numbers=self.wants_line_numbers_by_default(),
|
|
)
|
|
self._validate_token_limit(file_content, context_description)
|
|
content_parts.append(file_content)
|
|
|
|
# Track the expanded files as actually processed
|
|
actually_processed_files.extend(expanded_files)
|
|
|
|
# Estimate tokens for debug logging
|
|
from utils.token_utils import estimate_tokens
|
|
|
|
content_tokens = estimate_tokens(file_content)
|
|
logger.debug(
|
|
f"{self.name} tool successfully embedded {len(files_to_embed)} files ({content_tokens:,} tokens)"
|
|
)
|
|
logger.debug(f"[FILES] {self.name}: Successfully embedded files - {content_tokens:,} tokens used")
|
|
logger.debug(
|
|
f"[FILES] {self.name}: Actually processed {len(actually_processed_files)} individual files"
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"{self.name} tool failed to embed files {files_to_embed}: {type(e).__name__}: {e}")
|
|
logger.debug(f"[FILES] {self.name}: File embedding failed - {type(e).__name__}: {e}")
|
|
raise
|
|
else:
|
|
logger.debug(f"[FILES] {self.name}: No files to embed after filtering")
|
|
|
|
# Generate note about files already in conversation history
|
|
if continuation_id and len(files_to_embed) < len(request_files):
|
|
embedded_files = self.get_conversation_embedded_files(continuation_id)
|
|
skipped_files = [f for f in request_files if f in embedded_files]
|
|
if skipped_files:
|
|
logger.debug(
|
|
f"{self.name} tool skipping {len(skipped_files)} files already in conversation history: {', '.join(skipped_files)}"
|
|
)
|
|
logger.debug(f"[FILES] {self.name}: Adding note about {len(skipped_files)} skipped files")
|
|
if content_parts:
|
|
content_parts.append("\n\n")
|
|
note_lines = [
|
|
"--- NOTE: Additional files referenced in conversation history ---",
|
|
"The following files are already available in our conversation context:",
|
|
"\n".join(f" - {f}" for f in skipped_files),
|
|
"--- END NOTE ---",
|
|
]
|
|
content_parts.append("\n".join(note_lines))
|
|
else:
|
|
logger.debug(f"[FILES] {self.name}: No skipped files to note")
|
|
|
|
result = "".join(content_parts) if content_parts else ""
|
|
logger.debug(
|
|
f"[FILES] {self.name}: _prepare_file_content_for_prompt returning {len(result)} chars, {len(actually_processed_files)} processed files"
|
|
)
|
|
return result, actually_processed_files
|
|
|
|
def get_websearch_instruction(self, use_websearch: bool, tool_specific: Optional[str] = None) -> str:
|
|
"""
|
|
Generate standardized web search instruction based on the use_websearch parameter.
|
|
|
|
Args:
|
|
use_websearch: Whether web search is enabled
|
|
tool_specific: Optional tool-specific search guidance
|
|
|
|
Returns:
|
|
str: Web search instruction to append to prompt, or empty string
|
|
"""
|
|
if not use_websearch:
|
|
return ""
|
|
|
|
base_instruction = """
|
|
|
|
WEB SEARCH CAPABILITY: You can request Claude to perform web searches to enhance your analysis with current information!
|
|
|
|
IMPORTANT: When you identify areas where web searches would significantly improve your response (such as checking current documentation, finding recent solutions, verifying best practices, or gathering community insights), you MUST explicitly instruct Claude to perform specific web searches and then respond back using the continuation_id from this response to continue the analysis.
|
|
|
|
Use clear, direct language based on the value of the search:
|
|
|
|
For valuable supplementary information: "Please perform a web search on '[specific topic/query]' and then continue this analysis using the continuation_id from this response if you find relevant information."
|
|
|
|
For important missing information: "Please search for '[specific topic/query]' and respond back with the findings using the continuation_id from this response - this information is needed to provide a complete analysis."
|
|
|
|
For critical/essential information: "SEARCH REQUIRED: Please immediately perform a web search on '[specific topic/query]' and respond back with the results using the continuation_id from this response. Cannot provide accurate analysis without this current information."
|
|
|
|
This ensures you get the most current and comprehensive information while maintaining conversation context through the continuation_id."""
|
|
|
|
if tool_specific:
|
|
return f"""{base_instruction}
|
|
|
|
{tool_specific}
|
|
|
|
When recommending searches, be specific about what information you need and why it would improve your analysis."""
|
|
|
|
# Default instruction for all tools
|
|
return f"""{base_instruction}
|
|
|
|
Consider requesting searches for:
|
|
- Current documentation and API references
|
|
- Recent best practices and patterns
|
|
- Known issues and community solutions
|
|
- Framework updates and compatibility
|
|
- Security advisories and patches
|
|
- Performance benchmarks and optimizations
|
|
|
|
When recommending searches, be specific about what information you need and why it would improve your analysis. Always remember to instruct Claude to use the continuation_id from this response when providing search results."""
|
|
|
|
@abstractmethod
|
|
def get_request_model(self):
|
|
"""
|
|
Return the Pydantic model class used for validating requests.
|
|
|
|
This model should inherit from ToolRequest and define all
|
|
parameters specific to this tool.
|
|
|
|
Returns:
|
|
Type[ToolRequest]: The request model class
|
|
"""
|
|
pass
|
|
|
|
def validate_file_paths(self, request) -> Optional[str]:
|
|
"""
|
|
Validate that all file paths in the request are absolute.
|
|
|
|
This is a critical security function that prevents path traversal attacks
|
|
and ensures all file access is properly controlled. All file paths must
|
|
be absolute to avoid ambiguity and security issues.
|
|
|
|
Args:
|
|
request: The validated request object
|
|
|
|
Returns:
|
|
Optional[str]: Error message if validation fails, None if all paths are valid
|
|
"""
|
|
# Check if request has 'files' attribute (used by most tools)
|
|
if hasattr(request, "files") and request.files:
|
|
for file_path in request.files:
|
|
if not os.path.isabs(file_path):
|
|
return (
|
|
f"Error: All file paths must be FULL absolute paths to real files / folders - DO NOT SHORTEN. "
|
|
f"Received relative path: {file_path}\n"
|
|
f"Please provide the full absolute path starting with '/' (must be FULL absolute paths to real files / folders - DO NOT SHORTEN)"
|
|
)
|
|
|
|
# Check if request has 'path' attribute (used by review_changes tool)
|
|
if hasattr(request, "path") and request.path:
|
|
if not os.path.isabs(request.path):
|
|
return (
|
|
f"Error: Path must be FULL absolute paths to real files / folders - DO NOT SHORTEN. "
|
|
f"Received relative path: {request.path}\n"
|
|
f"Please provide the full absolute path starting with '/' (must be FULL absolute paths to real files / folders - DO NOT SHORTEN)"
|
|
)
|
|
|
|
return None
|
|
|
|
def check_prompt_size(self, text: str) -> Optional[dict[str, Any]]:
|
|
"""
|
|
Check if USER INPUT text is too large for MCP transport boundary.
|
|
|
|
IMPORTANT: This method should ONLY be used to validate user input that crosses
|
|
the Claude CLI ↔ MCP Server transport boundary. It should NOT be used to limit
|
|
internal MCP Server operations.
|
|
|
|
MCP Protocol Boundaries:
|
|
Claude CLI ←→ MCP Server ←→ External Model
|
|
↑ ↑
|
|
This limit applies here This is NOT limited
|
|
|
|
The MCP protocol has a combined request+response limit of ~25K tokens.
|
|
To ensure adequate space for MCP Server → Claude CLI responses, we limit
|
|
user input to 50K characters (roughly ~10-12K tokens). Larger user prompts
|
|
are handled by having Claude save them to prompt.txt files, bypassing MCP's
|
|
transport constraints while preserving response capacity.
|
|
|
|
What should be checked with this method:
|
|
- request.prompt field (user input from Claude CLI)
|
|
- prompt.txt file content (alternative user input)
|
|
- Other direct user input fields
|
|
|
|
What should NOT be checked with this method:
|
|
- System prompts added internally
|
|
- File content embedded by tools
|
|
- Conversation history from Redis
|
|
- Complete prompts sent to external models
|
|
|
|
Args:
|
|
text: The user input text to check (NOT internal prompt content)
|
|
|
|
Returns:
|
|
Optional[Dict[str, Any]]: Response asking for file handling if too large, None otherwise
|
|
"""
|
|
if text and len(text) > MCP_PROMPT_SIZE_LIMIT:
|
|
return {
|
|
"status": "resend_prompt",
|
|
"content": (
|
|
f"MANDATORY ACTION REQUIRED: The prompt is too large for MCP's token limits (>{MCP_PROMPT_SIZE_LIMIT:,} characters). "
|
|
"YOU MUST IMMEDIATELY save the prompt text to a temporary file named 'prompt.txt' in the working directory. "
|
|
"DO NOT attempt to shorten or modify the prompt. SAVE IT AS-IS to 'prompt.txt'. "
|
|
"Then resend the request with the absolute file path to 'prompt.txt' in the files parameter (must be FULL absolute path - DO NOT SHORTEN), "
|
|
"along with any other files you wish to share as context. Leave the prompt text itself empty or very brief in the new request. "
|
|
"This is the ONLY way to handle large prompts - you MUST follow these exact steps."
|
|
),
|
|
"content_type": "text",
|
|
"metadata": {
|
|
"prompt_size": len(text),
|
|
"limit": MCP_PROMPT_SIZE_LIMIT,
|
|
"instructions": "MANDATORY: Save prompt to 'prompt.txt' in current folder and include absolute path in files parameter. DO NOT modify or shorten the prompt.",
|
|
},
|
|
}
|
|
return None
|
|
|
|
def _validate_image_limits(
|
|
self, images: Optional[list[str]], model_name: str, continuation_id: Optional[str] = None
|
|
) -> Optional[dict]:
|
|
"""
|
|
Validate image size against model capabilities at MCP boundary.
|
|
|
|
This performs strict validation to ensure we don't exceed model-specific
|
|
image size limits. Uses capability-based validation with actual model
|
|
configuration rather than hard-coded limits.
|
|
|
|
Args:
|
|
images: List of image paths/data URLs to validate
|
|
model_name: Name of the model to check limits against
|
|
|
|
Returns:
|
|
Optional[dict]: Error response if validation fails, None if valid
|
|
"""
|
|
if not images:
|
|
return None
|
|
|
|
# Get model capabilities to check image support and size limits
|
|
try:
|
|
# Use the already-resolved provider from model context if available
|
|
if hasattr(self, "_model_context") and self._model_context:
|
|
provider = self._model_context.provider
|
|
capabilities = self._model_context.capabilities
|
|
else:
|
|
# Fallback for edge cases (e.g., direct test calls)
|
|
provider = self.get_model_provider(model_name)
|
|
capabilities = provider.get_capabilities(model_name)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to get capabilities for model {model_name}: {e}")
|
|
# Fall back to checking custom models configuration
|
|
capabilities = None
|
|
|
|
# Check if model supports images at all
|
|
supports_images = False
|
|
max_size_mb = 0.0
|
|
|
|
if capabilities:
|
|
supports_images = capabilities.supports_images
|
|
max_size_mb = capabilities.max_image_size_mb
|
|
else:
|
|
# Fall back to custom models configuration
|
|
try:
|
|
import json
|
|
from pathlib import Path
|
|
|
|
custom_models_path = Path(__file__).parent.parent / "conf" / "custom_models.json"
|
|
if custom_models_path.exists():
|
|
with open(custom_models_path) as f:
|
|
custom_config = json.load(f)
|
|
|
|
# Check if model is in custom models list
|
|
for model_config in custom_config.get("models", []):
|
|
if model_config.get("model_name") == model_name or model_name in model_config.get(
|
|
"aliases", []
|
|
):
|
|
supports_images = model_config.get("supports_images", False)
|
|
max_size_mb = model_config.get("max_image_size_mb", 0.0)
|
|
break
|
|
except Exception as e:
|
|
logger.warning(f"Failed to load custom models config: {e}")
|
|
|
|
# If model doesn't support images, reject
|
|
if not supports_images:
|
|
return {
|
|
"status": "error",
|
|
"content": (
|
|
f"Image support not available: Model '{model_name}' does not support image processing. "
|
|
f"Please use a vision-capable model such as 'gemini-2.5-flash', 'o3', "
|
|
f"or 'claude-3-opus' for image analysis tasks."
|
|
),
|
|
"content_type": "text",
|
|
"metadata": {
|
|
"error_type": "validation_error",
|
|
"model_name": model_name,
|
|
"supports_images": False,
|
|
"image_count": len(images),
|
|
},
|
|
}
|
|
|
|
# Calculate total size of all images
|
|
total_size_mb = 0.0
|
|
for image_path in images:
|
|
try:
|
|
if image_path.startswith("data:image/"):
|
|
# Handle data URL: data:image/png;base64,iVBORw0...
|
|
_, data = image_path.split(",", 1)
|
|
# Base64 encoding increases size by ~33%, so decode to get actual size
|
|
import base64
|
|
|
|
actual_size = len(base64.b64decode(data))
|
|
|
|
actual_size = len(base64.b64decode(data))
|
|
total_size_mb += actual_size / (1024 * 1024)
|
|
else:
|
|
# Handle file path
|
|
if os.path.exists(image_path):
|
|
file_size = os.path.getsize(image_path)
|
|
total_size_mb += file_size / (1024 * 1024)
|
|
else:
|
|
logger.warning(f"Image file not found: {image_path}")
|
|
# Assume a reasonable size for missing files to avoid breaking validation
|
|
total_size_mb += 1.0 # 1MB assumption
|
|
except Exception as e:
|
|
logger.warning(f"Failed to get size for image {image_path}: {e}")
|
|
# Assume a reasonable size for problematic files
|
|
total_size_mb += 1.0 # 1MB assumption
|
|
|
|
# Apply 40MB cap for custom models as requested
|
|
effective_limit_mb = max_size_mb
|
|
if hasattr(capabilities, "provider") and capabilities.provider == ProviderType.CUSTOM:
|
|
effective_limit_mb = min(max_size_mb, 40.0)
|
|
elif not capabilities: # Fallback case for custom models
|
|
effective_limit_mb = min(max_size_mb, 40.0)
|
|
|
|
# Validate against size limit
|
|
if total_size_mb > effective_limit_mb:
|
|
return {
|
|
"status": "error",
|
|
"content": (
|
|
f"Image size limit exceeded: Model '{model_name}' supports maximum {effective_limit_mb:.1f}MB "
|
|
f"for all images combined, but {total_size_mb:.1f}MB was provided. "
|
|
f"Please reduce image sizes or count and try again."
|
|
),
|
|
"content_type": "text",
|
|
"metadata": {
|
|
"error_type": "validation_error",
|
|
"model_name": model_name,
|
|
"total_size_mb": round(total_size_mb, 2),
|
|
"limit_mb": round(effective_limit_mb, 2),
|
|
"image_count": len(images),
|
|
"supports_images": supports_images,
|
|
},
|
|
}
|
|
|
|
# All validations passed
|
|
logger.debug(f"Image validation passed: {len(images)} images")
|
|
return None
|
|
|
|
def estimate_tokens_smart(self, file_path: str) -> int:
|
|
"""
|
|
Estimate tokens for a file using file-type aware ratios.
|
|
|
|
Args:
|
|
file_path: Path to the file
|
|
|
|
Returns:
|
|
int: Estimated token count
|
|
"""
|
|
from utils.file_utils import estimate_file_tokens
|
|
|
|
return estimate_file_tokens(file_path)
|
|
|
|
def check_total_file_size(self, files: list[str], model_name: str) -> Optional[dict[str, Any]]:
|
|
"""
|
|
Check if total file sizes would exceed token threshold before embedding.
|
|
|
|
IMPORTANT: This performs STRICT REJECTION at MCP boundary.
|
|
No partial inclusion - either all files fit or request is rejected.
|
|
This forces Claude to make better file selection decisions.
|
|
|
|
Args:
|
|
files: List of file paths to check
|
|
model_name: The resolved model name to use for token limits
|
|
|
|
Returns:
|
|
Dict with `code_too_large` response if too large, None if acceptable
|
|
"""
|
|
if not files:
|
|
return None
|
|
|
|
# Use centralized file size checking with model context
|
|
from utils.file_utils import check_total_file_size as check_file_size_utility
|
|
|
|
return check_file_size_utility(files, model_name)
|
|
|
|
def handle_prompt_file(self, files: Optional[list[str]]) -> tuple[Optional[str], Optional[list[str]]]:
|
|
"""
|
|
Check for and handle prompt.txt in the files list.
|
|
|
|
If prompt.txt is found, reads its content and removes it from the files list.
|
|
This file is treated specially as the main prompt, not as an embedded file.
|
|
|
|
This mechanism allows us to work around MCP's ~25K token limit by having
|
|
Claude save large prompts to a file, effectively using the file transfer
|
|
mechanism to bypass token constraints while preserving response capacity.
|
|
|
|
Args:
|
|
files: List of file paths (will be translated for current environment)
|
|
|
|
Returns:
|
|
tuple: (prompt_content, updated_files_list)
|
|
"""
|
|
if not files:
|
|
return None, files
|
|
|
|
prompt_content = None
|
|
updated_files = []
|
|
|
|
for file_path in files:
|
|
|
|
# Check if the filename is exactly "prompt.txt"
|
|
# This ensures we don't match files like "myprompt.txt" or "prompt.txt.bak"
|
|
if os.path.basename(file_path) == "prompt.txt":
|
|
try:
|
|
# Read prompt.txt content and extract just the text
|
|
content, _ = read_file_content(file_path)
|
|
# Extract the content between the file markers
|
|
if "--- BEGIN FILE:" in content and "--- END FILE:" in content:
|
|
lines = content.split("\n")
|
|
in_content = False
|
|
content_lines = []
|
|
for line in lines:
|
|
if line.startswith("--- BEGIN FILE:"):
|
|
in_content = True
|
|
continue
|
|
elif line.startswith("--- END FILE:"):
|
|
break
|
|
elif in_content:
|
|
content_lines.append(line)
|
|
prompt_content = "\n".join(content_lines)
|
|
else:
|
|
# Fallback: if it's already raw content (from tests or direct input)
|
|
# and doesn't have error markers, use it directly
|
|
if not content.startswith("\n--- ERROR"):
|
|
prompt_content = content
|
|
else:
|
|
prompt_content = None
|
|
except Exception:
|
|
# If we can't read the file, we'll just skip it
|
|
# The error will be handled elsewhere
|
|
pass
|
|
else:
|
|
# Keep the original path in the files list (will be translated later by read_files)
|
|
updated_files.append(file_path)
|
|
|
|
return prompt_content, updated_files if updated_files else None
|
|
|
|
async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
|
|
"""
|
|
Execute the tool with the provided arguments.
|
|
|
|
This is the main entry point for tool execution. It handles:
|
|
1. Request validation using the tool's Pydantic model
|
|
2. File path security validation
|
|
3. Prompt preparation
|
|
4. Model creation and configuration
|
|
5. Response generation and formatting
|
|
6. Error handling and recovery
|
|
|
|
Args:
|
|
arguments: Dictionary of arguments from the MCP client
|
|
|
|
Returns:
|
|
List[TextContent]: Formatted response as MCP TextContent objects
|
|
"""
|
|
try:
|
|
# Store arguments for access by helper methods (like _prepare_file_content_for_prompt)
|
|
self._current_arguments = arguments
|
|
|
|
# Set up logger for this tool execution
|
|
logger = logging.getLogger(f"tools.{self.name}")
|
|
logger.info(f"🔧 {self.name} tool called with arguments: {list(arguments.keys())}")
|
|
|
|
# Validate request using the tool's Pydantic model
|
|
# This ensures all required fields are present and properly typed
|
|
request_model = self.get_request_model()
|
|
request = request_model(**arguments)
|
|
logger.debug(f"Request validation successful for {self.name}")
|
|
|
|
# Validate file paths for security
|
|
# This prevents path traversal attacks and ensures proper access control
|
|
path_error = self.validate_file_paths(request)
|
|
if path_error:
|
|
error_output = ToolOutput(
|
|
status="error",
|
|
content=path_error,
|
|
content_type="text",
|
|
)
|
|
return [TextContent(type="text", text=error_output.model_dump_json())]
|
|
|
|
# Extract and validate images from request
|
|
images = getattr(request, "images", None) or []
|
|
|
|
# MODEL RESOLUTION NOW HAPPENS AT MCP BOUNDARY
|
|
# Extract pre-resolved model context from server.py
|
|
model_context = self._current_arguments.get("_model_context")
|
|
resolved_model_name = self._current_arguments.get("_resolved_model_name")
|
|
|
|
if model_context and resolved_model_name:
|
|
# Model was already resolved at MCP boundary
|
|
model_name = resolved_model_name
|
|
logger.debug(f"Using pre-resolved model '{model_name}' from MCP boundary")
|
|
else:
|
|
# Fallback for direct execute calls
|
|
model_name = getattr(request, "model", None)
|
|
if not model_name:
|
|
from config import DEFAULT_MODEL
|
|
|
|
model_name = DEFAULT_MODEL
|
|
logger.debug(f"Using fallback model resolution for '{model_name}' (test mode)")
|
|
|
|
# For tests: Check if we should require model selection (auto mode)
|
|
if self._should_require_model_selection(model_name):
|
|
# Get suggested model based on tool category
|
|
from providers.registry import ModelProviderRegistry
|
|
|
|
tool_category = self.get_model_category()
|
|
suggested_model = ModelProviderRegistry.get_preferred_fallback_model(tool_category)
|
|
|
|
# Build error message based on why selection is required
|
|
if model_name.lower() == "auto":
|
|
error_message = (
|
|
f"Model parameter is required in auto mode. "
|
|
f"Suggested model for {self.name}: '{suggested_model}' "
|
|
f"(category: {tool_category.value})"
|
|
)
|
|
else:
|
|
# Model was specified but not available
|
|
available_models = self._get_available_models()
|
|
|
|
error_message = (
|
|
f"Model '{model_name}' is not available with current API keys. "
|
|
f"Available models: {', '.join(available_models)}. "
|
|
f"Suggested model for {self.name}: '{suggested_model}' "
|
|
f"(category: {tool_category.value})"
|
|
)
|
|
error_output = ToolOutput(
|
|
status="error",
|
|
content=error_message,
|
|
content_type="text",
|
|
)
|
|
return [TextContent(type="text", text=error_output.model_dump_json())]
|
|
|
|
# Create model context for tests
|
|
from utils.model_context import ModelContext
|
|
|
|
model_context = ModelContext(model_name)
|
|
|
|
# Store resolved model name for use by helper methods
|
|
self._current_model_name = model_name
|
|
self._model_context = model_context
|
|
|
|
# Check if we have continuation_id - if so, conversation history is already embedded
|
|
continuation_id = getattr(request, "continuation_id", None)
|
|
|
|
if continuation_id:
|
|
# When continuation_id is present, server.py has already injected the
|
|
# conversation history into the appropriate field. We need to check if
|
|
# the prompt already contains conversation history marker.
|
|
logger.debug(f"Continuing {self.name} conversation with thread {continuation_id}")
|
|
|
|
# Store the original arguments to detect enhanced prompts
|
|
self._has_embedded_history = False
|
|
|
|
# Check if conversation history is already embedded in the prompt field
|
|
field_value = getattr(request, "prompt", "")
|
|
field_name = "prompt"
|
|
|
|
if "=== CONVERSATION HISTORY ===" in field_value:
|
|
# Conversation history is already embedded, use it directly
|
|
prompt = field_value
|
|
self._has_embedded_history = True
|
|
logger.debug(f"{self.name}: Using pre-embedded conversation history from {field_name}")
|
|
else:
|
|
# No embedded history, prepare prompt normally
|
|
prompt = await self.prepare_prompt(request)
|
|
logger.debug(f"{self.name}: No embedded history found, prepared prompt normally")
|
|
else:
|
|
# New conversation, prepare prompt normally
|
|
prompt = await self.prepare_prompt(request)
|
|
|
|
# Add follow-up instructions for new conversations
|
|
from server import get_follow_up_instructions
|
|
|
|
follow_up_instructions = get_follow_up_instructions(0) # New conversation, turn 0
|
|
prompt = f"{prompt}\n\n{follow_up_instructions}"
|
|
logger.debug(f"Added follow-up instructions for new {self.name} conversation")
|
|
|
|
# Model name already resolved and stored in self._current_model_name earlier
|
|
|
|
# Validate images at MCP boundary if any were provided
|
|
if images:
|
|
image_validation_error = self._validate_image_limits(images, self._current_model_name, continuation_id)
|
|
if image_validation_error:
|
|
return [TextContent(type="text", text=json.dumps(image_validation_error))]
|
|
|
|
temperature = getattr(request, "temperature", None)
|
|
if temperature is None:
|
|
temperature = self.get_default_temperature()
|
|
thinking_mode = getattr(request, "thinking_mode", None)
|
|
if thinking_mode is None:
|
|
thinking_mode = self.get_default_thinking_mode()
|
|
|
|
# Get the appropriate model provider
|
|
provider = self.get_model_provider(self._current_model_name)
|
|
|
|
# Validate and correct temperature for this model
|
|
temperature, temp_warnings = self._validate_and_correct_temperature(self._current_model_name, temperature)
|
|
|
|
# Log any temperature corrections
|
|
for warning in temp_warnings:
|
|
logger.warning(warning)
|
|
|
|
# Get system prompt for this tool
|
|
system_prompt = self.get_system_prompt()
|
|
|
|
# Generate AI response using the provider
|
|
logger.info(f"Sending request to {provider.get_provider_type().value} API for {self.name}")
|
|
logger.info(f"Using model: {self._current_model_name} via {provider.get_provider_type().value} provider")
|
|
|
|
# Import token estimation utility
|
|
from utils.token_utils import estimate_tokens
|
|
|
|
estimated_tokens = estimate_tokens(prompt)
|
|
logger.debug(f"Prompt length: {len(prompt)} characters (~{estimated_tokens:,} tokens)")
|
|
|
|
# Generate content with provider abstraction
|
|
model_response = provider.generate_content(
|
|
prompt=prompt,
|
|
model_name=self._current_model_name,
|
|
system_prompt=system_prompt,
|
|
temperature=temperature,
|
|
thinking_mode=thinking_mode if provider.supports_thinking_mode(self._current_model_name) else None,
|
|
images=images if images else None, # Pass images via kwargs
|
|
)
|
|
|
|
logger.info(f"Received response from {provider.get_provider_type().value} API for {self.name}")
|
|
|
|
# Process the model's response
|
|
if model_response.content:
|
|
raw_text = model_response.content
|
|
|
|
# Parse response to check for clarification requests or format output
|
|
# Pass model info for conversation tracking
|
|
model_info = {
|
|
"provider": provider,
|
|
"model_name": self._current_model_name,
|
|
"model_response": model_response,
|
|
}
|
|
tool_output = self._parse_response(raw_text, request, model_info)
|
|
logger.info(f"✅ {self.name} tool completed successfully")
|
|
|
|
else:
|
|
# Handle cases where the model couldn't generate a response
|
|
# This might happen due to safety filters or other constraints
|
|
finish_reason = model_response.metadata.get("finish_reason", "Unknown")
|
|
logger.warning(f"Response blocked or incomplete for {self.name}. Finish reason: {finish_reason}")
|
|
tool_output = ToolOutput(
|
|
status="error",
|
|
content=f"Response blocked or incomplete. Finish reason: {finish_reason}",
|
|
content_type="text",
|
|
)
|
|
|
|
# Return standardized JSON response for consistent client handling
|
|
return [TextContent(type="text", text=tool_output.model_dump_json())]
|
|
|
|
except Exception as e:
|
|
# Catch all exceptions to prevent server crashes
|
|
# Return error information in standardized format
|
|
logger = logging.getLogger(f"tools.{self.name}")
|
|
error_msg = str(e)
|
|
|
|
# Check if this is an MCP size check error from prepare_prompt
|
|
if error_msg.startswith("MCP_SIZE_CHECK:"):
|
|
logger.info(f"MCP prompt size limit exceeded in {self.name}")
|
|
tool_output_json = error_msg[15:] # Remove "MCP_SIZE_CHECK:" prefix
|
|
return [TextContent(type="text", text=tool_output_json)]
|
|
|
|
# Check if this is a 500 INTERNAL error that asks for retry
|
|
if "500 INTERNAL" in error_msg and "Please retry" in error_msg:
|
|
logger.warning(f"500 INTERNAL error in {self.name} - attempting retry")
|
|
try:
|
|
# Single retry attempt using provider
|
|
retry_response = provider.generate_content(
|
|
prompt=prompt,
|
|
model_name=model_name,
|
|
system_prompt=system_prompt,
|
|
temperature=temperature,
|
|
thinking_mode=thinking_mode if provider.supports_thinking_mode(model_name) else None,
|
|
images=images if images else None, # Pass images via kwargs in retry too
|
|
)
|
|
|
|
if retry_response.content:
|
|
# If successful, process normally
|
|
retry_model_info = {
|
|
"provider": provider,
|
|
"model_name": model_name,
|
|
"model_response": retry_response,
|
|
}
|
|
tool_output = self._parse_response(retry_response.content, request, retry_model_info)
|
|
return [TextContent(type="text", text=tool_output.model_dump_json())]
|
|
|
|
except Exception as retry_e:
|
|
logger.error(f"Retry failed for {self.name} tool: {str(retry_e)}")
|
|
error_msg = f"Tool failed after retry: {str(retry_e)}"
|
|
|
|
logger.error(f"Error in {self.name} tool execution: {error_msg}", exc_info=True)
|
|
|
|
error_output = ToolOutput(
|
|
status="error",
|
|
content=f"Error in {self.name}: {error_msg}",
|
|
content_type="text",
|
|
)
|
|
return [TextContent(type="text", text=error_output.model_dump_json())]
|
|
|
|
def _parse_response(self, raw_text: str, request, model_info: Optional[dict] = None) -> ToolOutput:
|
|
"""
|
|
Parse the raw response and check for clarification requests.
|
|
|
|
This method formats the response and always offers a continuation opportunity
|
|
unless max conversation turns have been reached.
|
|
|
|
Args:
|
|
raw_text: The raw text response from the model
|
|
request: The original request for context
|
|
model_info: Optional dict with model metadata
|
|
|
|
Returns:
|
|
ToolOutput: Standardized output object
|
|
"""
|
|
logger = logging.getLogger(f"tools.{self.name}")
|
|
|
|
try:
|
|
# Try to parse as JSON to check for special status requests
|
|
potential_json = json.loads(raw_text.strip())
|
|
|
|
if isinstance(potential_json, dict) and "status" in potential_json:
|
|
status_key = potential_json.get("status")
|
|
status_model = SPECIAL_STATUS_MODELS.get(status_key)
|
|
|
|
if status_model:
|
|
try:
|
|
# Use Pydantic for robust validation of the special status
|
|
parsed_status = status_model.model_validate(potential_json)
|
|
logger.debug(f"{self.name} tool detected special status: {status_key}")
|
|
|
|
# Enhance mandatory_instructions for files_required_to_continue
|
|
if status_key == "files_required_to_continue" and hasattr(
|
|
parsed_status, "mandatory_instructions"
|
|
):
|
|
original_instructions = parsed_status.mandatory_instructions
|
|
enhanced_instructions = self._enhance_mandatory_instructions(original_instructions)
|
|
# Create a new model instance with enhanced instructions
|
|
enhanced_data = parsed_status.model_dump()
|
|
enhanced_data["mandatory_instructions"] = enhanced_instructions
|
|
parsed_status = status_model.model_validate(enhanced_data)
|
|
|
|
# Extract model information for metadata
|
|
metadata = {
|
|
"original_request": (
|
|
request.model_dump() if hasattr(request, "model_dump") else str(request)
|
|
)
|
|
}
|
|
if model_info:
|
|
model_name = model_info.get("model_name")
|
|
if model_name:
|
|
metadata["model_used"] = model_name
|
|
|
|
return ToolOutput(
|
|
status=status_key,
|
|
content=parsed_status.model_dump_json(),
|
|
content_type="json",
|
|
metadata=metadata,
|
|
)
|
|
|
|
except Exception as e:
|
|
# Invalid payload for known status, log warning and continue as normal response
|
|
logger.warning(f"Invalid {status_key} payload: {e}")
|
|
|
|
except (json.JSONDecodeError, ValueError, TypeError):
|
|
# Not a JSON special status request, treat as normal response
|
|
pass
|
|
|
|
# Normal text response - format using tool-specific formatting
|
|
formatted_content = self.format_response(raw_text, request, model_info)
|
|
|
|
# Always check if we should offer Claude a continuation opportunity
|
|
continuation_offer = self._check_continuation_opportunity(request)
|
|
|
|
if continuation_offer:
|
|
logger.debug(
|
|
f"Creating continuation offer for {self.name} with {continuation_offer['remaining_turns']} turns remaining"
|
|
)
|
|
return self._create_continuation_offer_response(formatted_content, continuation_offer, request, model_info)
|
|
else:
|
|
logger.debug(f"No continuation offer created for {self.name} - max turns reached")
|
|
|
|
# If this is a threaded conversation (has continuation_id), save the response
|
|
continuation_id = getattr(request, "continuation_id", None)
|
|
if continuation_id:
|
|
request_files = getattr(request, "files", []) or []
|
|
request_images = getattr(request, "images", []) or []
|
|
# Extract model metadata for conversation tracking
|
|
model_provider = None
|
|
model_name = None
|
|
model_metadata = None
|
|
|
|
if model_info:
|
|
provider = model_info.get("provider")
|
|
if provider:
|
|
model_provider = provider.get_provider_type().value
|
|
model_name = model_info.get("model_name")
|
|
model_response = model_info.get("model_response")
|
|
if model_response:
|
|
model_metadata = {"usage": model_response.usage, "metadata": model_response.metadata}
|
|
|
|
success = add_turn(
|
|
continuation_id,
|
|
"assistant",
|
|
formatted_content,
|
|
files=request_files,
|
|
images=request_images,
|
|
tool_name=self.name,
|
|
model_provider=model_provider,
|
|
model_name=model_name,
|
|
model_metadata=model_metadata,
|
|
)
|
|
if not success:
|
|
logging.warning(f"Failed to add turn to thread {continuation_id} for {self.name}")
|
|
|
|
# Determine content type based on the formatted content
|
|
content_type = (
|
|
"markdown" if any(marker in formatted_content for marker in ["##", "**", "`", "- ", "1. "]) else "text"
|
|
)
|
|
|
|
# Extract model information for metadata
|
|
metadata = {"tool_name": self.name}
|
|
if model_info:
|
|
model_name = model_info.get("model_name")
|
|
if model_name:
|
|
metadata["model_used"] = model_name
|
|
|
|
return ToolOutput(
|
|
status="success",
|
|
content=formatted_content,
|
|
content_type=content_type,
|
|
metadata=metadata,
|
|
)
|
|
|
|
def _check_continuation_opportunity(self, request) -> Optional[dict]:
|
|
"""
|
|
Check if we should offer Claude a continuation opportunity.
|
|
|
|
This is called when Gemini doesn't ask a follow-up question, but we want
|
|
to give Claude the chance to continue the conversation if needed.
|
|
|
|
Args:
|
|
request: The original request
|
|
|
|
Returns:
|
|
Dict with continuation data if opportunity should be offered, None otherwise
|
|
"""
|
|
# Skip continuation offers in test mode
|
|
import os
|
|
|
|
if os.getenv("PYTEST_CURRENT_TEST"):
|
|
return None
|
|
|
|
continuation_id = getattr(request, "continuation_id", None)
|
|
|
|
try:
|
|
if continuation_id:
|
|
# Check remaining turns in thread chain
|
|
from utils.conversation_memory import get_thread_chain
|
|
|
|
chain = get_thread_chain(continuation_id)
|
|
if chain:
|
|
# Count total turns across all threads in chain
|
|
total_turns = sum(len(thread.turns) for thread in chain)
|
|
remaining_turns = MAX_CONVERSATION_TURNS - total_turns - 1 # -1 for this response
|
|
else:
|
|
# Thread not found, don't offer continuation
|
|
return None
|
|
else:
|
|
# New conversation, we have MAX_CONVERSATION_TURNS - 1 remaining
|
|
# (since this response will be turn 1)
|
|
remaining_turns = MAX_CONVERSATION_TURNS - 1
|
|
|
|
if remaining_turns <= 0:
|
|
return None
|
|
|
|
# Offer continuation opportunity
|
|
return {"remaining_turns": remaining_turns, "tool_name": self.name}
|
|
except Exception:
|
|
# If anything fails, don't offer continuation
|
|
return None
|
|
|
|
def _create_continuation_offer_response(
|
|
self, content: str, continuation_data: dict, request, model_info: Optional[dict] = None
|
|
) -> ToolOutput:
|
|
"""
|
|
Create a response offering Claude the opportunity to continue conversation.
|
|
|
|
Args:
|
|
content: The main response content
|
|
continuation_data: Dict containing remaining_turns and tool_name
|
|
request: Original request for context
|
|
|
|
Returns:
|
|
ToolOutput configured with continuation offer
|
|
"""
|
|
try:
|
|
# Create new thread for potential continuation (with parent link if continuing)
|
|
continuation_id = getattr(request, "continuation_id", None)
|
|
thread_id = create_thread(
|
|
tool_name=self.name,
|
|
initial_request=request.model_dump() if hasattr(request, "model_dump") else {},
|
|
parent_thread_id=continuation_id, # Link to parent if this is a continuation
|
|
)
|
|
|
|
# Add this response as the first turn (assistant turn)
|
|
# Use actually processed files from file preparation instead of original request files
|
|
# This ensures directories are tracked as their individual expanded files
|
|
request_files = getattr(self, "_actually_processed_files", []) or getattr(request, "files", []) or []
|
|
request_images = getattr(request, "images", []) or []
|
|
# Extract model metadata
|
|
model_provider = None
|
|
model_name = None
|
|
model_metadata = None
|
|
|
|
if model_info:
|
|
provider = model_info.get("provider")
|
|
if provider:
|
|
model_provider = provider.get_provider_type().value
|
|
model_name = model_info.get("model_name")
|
|
model_response = model_info.get("model_response")
|
|
if model_response:
|
|
model_metadata = {"usage": model_response.usage, "metadata": model_response.metadata}
|
|
|
|
add_turn(
|
|
thread_id,
|
|
"assistant",
|
|
content,
|
|
files=request_files,
|
|
images=request_images,
|
|
tool_name=self.name,
|
|
model_provider=model_provider,
|
|
model_name=model_name,
|
|
model_metadata=model_metadata,
|
|
)
|
|
|
|
# Create continuation offer
|
|
remaining_turns = continuation_data["remaining_turns"]
|
|
continuation_offer = ContinuationOffer(
|
|
continuation_id=thread_id,
|
|
note=(
|
|
f"If you'd like to continue this discussion or need to provide me with further details or context, "
|
|
f"you can use the continuation_id '{thread_id}' with any tool and any model. "
|
|
f"You have {remaining_turns} more exchange(s) available in this conversation thread."
|
|
),
|
|
suggested_tool_params={
|
|
"continuation_id": thread_id,
|
|
"prompt": "[Your follow-up question, additional context, or further details]",
|
|
},
|
|
remaining_turns=remaining_turns,
|
|
)
|
|
|
|
# Extract model information for metadata
|
|
metadata = {"tool_name": self.name, "thread_id": thread_id, "remaining_turns": remaining_turns}
|
|
if model_info:
|
|
model_name = model_info.get("model_name")
|
|
if model_name:
|
|
metadata["model_used"] = model_name
|
|
|
|
return ToolOutput(
|
|
status="continuation_available",
|
|
content=content,
|
|
content_type="markdown",
|
|
continuation_offer=continuation_offer,
|
|
metadata=metadata,
|
|
)
|
|
|
|
except Exception as e:
|
|
# If threading fails, return normal response but log the error
|
|
logger = logging.getLogger(f"tools.{self.name}")
|
|
logger.warning(f"Conversation threading failed in {self.name}: {str(e)}")
|
|
# Extract model information for metadata
|
|
metadata = {"tool_name": self.name, "threading_error": str(e)}
|
|
if model_info:
|
|
model_name = model_info.get("model_name")
|
|
if model_name:
|
|
metadata["model_used"] = model_name
|
|
|
|
return ToolOutput(
|
|
status="success",
|
|
content=content,
|
|
content_type="markdown",
|
|
metadata=metadata,
|
|
)
|
|
|
|
@abstractmethod
|
|
async def prepare_prompt(self, request) -> str:
|
|
"""
|
|
Prepare the complete prompt for the Gemini model.
|
|
|
|
This method should combine the system prompt with the user's request
|
|
and any additional context (like file contents) needed for the task.
|
|
|
|
Args:
|
|
request: The validated request object
|
|
|
|
Returns:
|
|
str: Complete prompt ready for the model
|
|
"""
|
|
pass
|
|
|
|
def format_response(self, response: str, request, model_info: Optional[dict] = None) -> str:
|
|
"""
|
|
Format the model's response for display.
|
|
|
|
Override this method to add tool-specific formatting like headers,
|
|
summaries, or structured output. Default implementation returns
|
|
the response unchanged.
|
|
|
|
Args:
|
|
response: The raw response from the model
|
|
request: The original request for context
|
|
model_info: Optional dict with model metadata (provider, model_name, model_response)
|
|
|
|
Returns:
|
|
str: Formatted response
|
|
"""
|
|
return response
|
|
|
|
def _validate_token_limit(self, text: str, context_type: str = "Context", context_window: int = 200_000) -> None:
|
|
"""
|
|
Validate token limit and raise ValueError if exceeded.
|
|
|
|
This centralizes the token limit check that was previously duplicated
|
|
in all prepare_prompt methods across tools.
|
|
|
|
Args:
|
|
text: The text to check
|
|
context_type: Description of what's being checked (for error message)
|
|
context_window: The model's context window size
|
|
|
|
Raises:
|
|
ValueError: If text exceeds context_window
|
|
"""
|
|
within_limit, estimated_tokens = check_token_limit(text, context_window)
|
|
if not within_limit:
|
|
raise ValueError(
|
|
f"{context_type} too large (~{estimated_tokens:,} tokens). Maximum is {context_window:,} tokens."
|
|
)
|
|
|
|
def _validate_and_correct_temperature(self, model_name: str, temperature: float) -> tuple[float, list[str]]:
|
|
"""
|
|
Validate and correct temperature for the specified model.
|
|
|
|
Args:
|
|
model_name: Name of the model to validate temperature for
|
|
temperature: Temperature value to validate
|
|
|
|
Returns:
|
|
Tuple of (corrected_temperature, warning_messages)
|
|
"""
|
|
try:
|
|
# Use the already-resolved provider and capabilities from model context
|
|
if hasattr(self, "_model_context") and self._model_context:
|
|
capabilities = self._model_context.capabilities
|
|
else:
|
|
# Fallback for edge cases (e.g., direct test calls)
|
|
provider = self.get_model_provider(model_name)
|
|
capabilities = provider.get_capabilities(model_name)
|
|
|
|
constraint = capabilities.temperature_constraint
|
|
|
|
warnings = []
|
|
|
|
if not constraint.validate(temperature):
|
|
corrected = constraint.get_corrected_value(temperature)
|
|
warning = (
|
|
f"Temperature {temperature} invalid for {model_name}. "
|
|
f"{constraint.get_description()}. Using {corrected} instead."
|
|
)
|
|
warnings.append(warning)
|
|
return corrected, warnings
|
|
|
|
return temperature, warnings
|
|
|
|
except Exception as e:
|
|
# If validation fails for any reason, use the original temperature
|
|
# and log a warning (but don't fail the request)
|
|
logger = logging.getLogger(f"tools.{self.name}")
|
|
logger.warning(f"Temperature validation failed for {model_name}: {e}")
|
|
return temperature, [f"Temperature validation failed: {e}"]
|
|
|
|
def get_model_provider(self, model_name: str) -> ModelProvider:
|
|
"""
|
|
Get a model provider for the specified model.
|
|
|
|
Args:
|
|
model_name: Name of the model to use (can be provider-specific or generic)
|
|
|
|
Returns:
|
|
ModelProvider instance configured for the model
|
|
|
|
Raises:
|
|
ValueError: If no provider supports the requested model
|
|
"""
|
|
# Get provider from registry
|
|
provider = ModelProviderRegistry.get_provider_for_model(model_name)
|
|
|
|
if not provider:
|
|
# Try to determine provider from model name patterns
|
|
if "gemini" in model_name.lower() or model_name.lower() in ["flash", "pro"]:
|
|
# Register Gemini provider if not already registered
|
|
from providers.base import ProviderType
|
|
from providers.gemini import GeminiModelProvider
|
|
|
|
ModelProviderRegistry.register_provider(ProviderType.GOOGLE, GeminiModelProvider)
|
|
provider = ModelProviderRegistry.get_provider(ProviderType.GOOGLE)
|
|
elif "gpt" in model_name.lower() or "o3" in model_name.lower():
|
|
# Register OpenAI provider if not already registered
|
|
from providers.base import ProviderType
|
|
from providers.openai_provider import OpenAIModelProvider
|
|
|
|
ModelProviderRegistry.register_provider(ProviderType.OPENAI, OpenAIModelProvider)
|
|
provider = ModelProviderRegistry.get_provider(ProviderType.OPENAI)
|
|
|
|
if not provider:
|
|
raise ValueError(
|
|
f"No provider found for model '{model_name}'. "
|
|
f"Ensure the appropriate API key is set and the model name is correct."
|
|
)
|
|
|
|
return provider
|
|
|
|
def _enhance_mandatory_instructions(self, original_instructions: str) -> str:
|
|
"""
|
|
Enhance mandatory instructions for files_required_to_continue responses.
|
|
|
|
This adds generic guidance to help Claude understand the importance
|
|
of providing the requested files and context.
|
|
|
|
Args:
|
|
original_instructions: The original instructions from the model
|
|
|
|
Returns:
|
|
str: Enhanced instructions with additional guidance
|
|
"""
|
|
generic_guidance = (
|
|
"\n\nIMPORTANT GUIDANCE:\n"
|
|
"• The requested files are CRITICAL for providing accurate analysis\n"
|
|
"• Please include ALL files mentioned in the files_needed list\n"
|
|
"• Use FULL absolute paths to real files/folders - DO NOT SHORTEN paths - and confirm that these exist\n"
|
|
"• If you cannot locate specific files or the files are extremely large, think hard, study the code and provide similar/related files that might contain the needed information\n"
|
|
"• After providing the files, use the same tool again with the continuation_id to continue the analysis\n"
|
|
"• The tool cannot proceed to perform its function accurately without this additional context"
|
|
)
|
|
|
|
return f"{original_instructions}{generic_guidance}"
|