7ea790ef88
- Fixed review_changes tool to properly translate host paths to container paths in Docker - Prevents "No such file or directory" errors when running in Docker containers - Added proper error handling with clear messages when paths are inaccessible refactor: Centralized token limit validation across all tools - Added _validate_token_limit method to BaseTool to eliminate code duplication - Reduced ~25 lines of duplicated code across 5 tools (analyze, chat, debug_issue, review_code, think_deeper) - Maintains exact same error messages and behavior feat: Enhanced large prompt handling - Added support for prompts >50K chars by requesting file-based input - Preserves MCP's ~25K token capacity for responses - All tools now check prompt size before processing test: Added comprehensive Docker path integration tests - Tests for path translation, security validation, and error handling - Tests for review_changes tool specifically with Docker paths - Fixed failing think_deeper test (updated default from "max" to "high") chore: Code quality improvements - Applied black formatting across all files - Fixed import sorting with isort - All tests passing (96 tests) - Standardized error handling follows MCP TextContent format The changes ensure consistent behavior across all environments while reducing code duplication and improving maintainability. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
634 lines
24 KiB
Python
634 lines
24 KiB
Python
"""
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Base class for all Gemini 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 os
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Literal, Optional
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from google import genai
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from google.genai import types
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from mcp.types import TextContent
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from pydantic import BaseModel, Field
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from config import MCP_PROMPT_SIZE_LIMIT
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from utils.file_utils import read_file_content
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from .models import ClarificationRequest, ToolOutput
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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, description="Model to use (defaults to Gemini 2.5 Pro)"
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)
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temperature: Optional[float] = Field(
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None, description="Temperature for response (tool-specific defaults)"
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)
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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="Thinking depth: minimal (128), low (2048), medium (8192), high (16384), max (32768)",
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)
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class BaseTool(ABC):
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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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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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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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@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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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 get_default_thinking_mode(self) -> str:
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"""
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Return the default thinking mode for this tool.
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Thinking mode controls computational budget for reasoning.
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Override for tools that need more or less reasoning depth.
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Returns:
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str: One of "minimal", "low", "medium", "high", "max"
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"""
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return "medium" # Default to medium thinking for better reasoning
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@abstractmethod
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def get_request_model(self):
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"""
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Return the Pydantic model class used for validating requests.
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This model should inherit from ToolRequest and define all
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parameters specific to this tool.
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Returns:
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Type[ToolRequest]: The request model class
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"""
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pass
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def validate_file_paths(self, request) -> Optional[str]:
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"""
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Validate that all file paths in the request are absolute.
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This is a critical security function that prevents path traversal attacks
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and ensures all file access is properly controlled. All file paths must
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be absolute to avoid ambiguity and security issues.
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Args:
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request: The validated request object
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Returns:
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Optional[str]: Error message if validation fails, None if all paths are valid
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"""
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# Check if request has 'files' attribute (used by most tools)
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if hasattr(request, "files") and request.files:
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for file_path in request.files:
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if not os.path.isabs(file_path):
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return (
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f"Error: All file paths must be absolute. "
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f"Received relative path: {file_path}\n"
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f"Please provide the full absolute path starting with '/'"
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)
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# Check if request has 'path' attribute (used by review_changes tool)
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if hasattr(request, "path") and request.path:
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if not os.path.isabs(request.path):
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return (
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f"Error: Path must be absolute. "
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f"Received relative path: {request.path}\n"
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f"Please provide the full absolute path starting with '/'"
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)
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return None
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def check_prompt_size(self, text: str) -> Optional[Dict[str, Any]]:
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"""
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Check if a text field is too large for MCP's token limits.
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The MCP protocol has a combined request+response limit of ~25K tokens.
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To ensure adequate space for responses, we limit prompt input to a
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configurable character limit (default 50K chars ~= 10-12K tokens).
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Larger prompts are handled by having Claude save them to a file,
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bypassing MCP's token constraints while preserving response capacity.
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Args:
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text: The text to check
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Returns:
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Optional[Dict[str, Any]]: Response asking for file handling if too large, None otherwise
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"""
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if text and len(text) > MCP_PROMPT_SIZE_LIMIT:
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return {
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"status": "requires_file_prompt",
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"content": (
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f"The prompt is too large for MCP's token limits (>{MCP_PROMPT_SIZE_LIMIT:,} characters). "
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"Please save the prompt text to a temporary file named 'prompt.txt' and "
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"resend the request with an empty prompt string and the absolute file path included "
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"in the files parameter, along with any other files you wish to share as context."
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),
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"content_type": "text",
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"metadata": {
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"prompt_size": len(text),
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"limit": MCP_PROMPT_SIZE_LIMIT,
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"instructions": "Save prompt to 'prompt.txt' and include absolute path in files parameter",
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},
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}
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return None
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def handle_prompt_file(
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self, files: Optional[List[str]]
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) -> tuple[Optional[str], Optional[List[str]]]:
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"""
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Check for and handle prompt.txt in the files list.
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If prompt.txt is found, reads its content and removes it from the files list.
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This file is treated specially as the main prompt, not as an embedded file.
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This mechanism allows us to work around MCP's ~25K token limit by having
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Claude save large prompts to a file, effectively using the file transfer
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mechanism to bypass token constraints while preserving response capacity.
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Args:
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files: List of file paths
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Returns:
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tuple: (prompt_content, updated_files_list)
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"""
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if not files:
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return None, files
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prompt_content = None
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updated_files = []
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for file_path in files:
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# Check if the filename is exactly "prompt.txt"
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# This ensures we don't match files like "myprompt.txt" or "prompt.txt.bak"
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if os.path.basename(file_path) == "prompt.txt":
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try:
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prompt_content = read_file_content(file_path)
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except Exception:
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# If we can't read the file, we'll just skip it
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# The error will be handled elsewhere
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pass
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else:
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updated_files.append(file_path)
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return prompt_content, updated_files if updated_files else None
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async def execute(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""
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Execute the tool with the provided arguments.
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This is the main entry point for tool execution. It handles:
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1. Request validation using the tool's Pydantic model
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2. File path security validation
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3. Prompt preparation
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4. Model creation and configuration
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5. Response generation and formatting
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6. Error handling and recovery
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Args:
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arguments: Dictionary of arguments from the MCP client
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Returns:
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List[TextContent]: Formatted response as MCP TextContent objects
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"""
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try:
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# Validate request using the tool's Pydantic model
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# This ensures all required fields are present and properly typed
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request_model = self.get_request_model()
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request = request_model(**arguments)
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# Validate file paths for security
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# This prevents path traversal attacks and ensures proper access control
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path_error = self.validate_file_paths(request)
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if path_error:
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error_output = ToolOutput(
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status="error",
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content=path_error,
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content_type="text",
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)
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return [TextContent(type="text", text=error_output.model_dump_json())]
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# Prepare the full prompt by combining system prompt with user request
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# This is delegated to the tool implementation for customization
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prompt = await self.prepare_prompt(request)
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# Extract model configuration from request or use defaults
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from config import GEMINI_MODEL
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model_name = getattr(request, "model", None) or GEMINI_MODEL
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temperature = getattr(request, "temperature", None)
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if temperature is None:
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temperature = self.get_default_temperature()
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thinking_mode = getattr(request, "thinking_mode", None)
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if thinking_mode is None:
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thinking_mode = self.get_default_thinking_mode()
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# Create model instance with appropriate configuration
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# This handles both regular models and thinking-enabled models
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model = self.create_model(model_name, temperature, thinking_mode)
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# Generate AI response using the configured model
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response = model.generate_content(prompt)
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# Process the model's response
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if response.candidates and response.candidates[0].content.parts:
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raw_text = response.candidates[0].content.parts[0].text
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# Parse response to check for clarification requests or format output
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tool_output = self._parse_response(raw_text, request)
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else:
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# Handle cases where the model couldn't generate a response
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# This might happen due to safety filters or other constraints
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finish_reason = (
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response.candidates[0].finish_reason
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if response.candidates
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else "Unknown"
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)
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tool_output = ToolOutput(
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status="error",
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content=f"Response blocked or incomplete. Finish reason: {finish_reason}",
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content_type="text",
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)
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# Return standardized JSON response for consistent client handling
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return [TextContent(type="text", text=tool_output.model_dump_json())]
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except Exception as e:
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# Catch all exceptions to prevent server crashes
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# Return error information in standardized format
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error_output = ToolOutput(
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status="error",
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content=f"Error in {self.name}: {str(e)}",
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content_type="text",
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)
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return [TextContent(type="text", text=error_output.model_dump_json())]
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def _parse_response(self, raw_text: str, request) -> ToolOutput:
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"""
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Parse the raw response and determine if it's a clarification request.
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Some tools may return JSON indicating they need more information.
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This method detects such responses and formats them appropriately.
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Args:
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raw_text: The raw text response from the model
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request: The original request for context
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Returns:
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ToolOutput: Standardized output object
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"""
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try:
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# Try to parse as JSON to check for clarification requests
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potential_json = json.loads(raw_text.strip())
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if (
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isinstance(potential_json, dict)
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and potential_json.get("status") == "requires_clarification"
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):
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# Validate the clarification request structure
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clarification = ClarificationRequest(**potential_json)
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return ToolOutput(
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status="requires_clarification",
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content=clarification.model_dump_json(),
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content_type="json",
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metadata={
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"original_request": (
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request.model_dump()
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if hasattr(request, "model_dump")
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else str(request)
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)
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},
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)
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except (json.JSONDecodeError, ValueError, TypeError):
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# Not a JSON clarification request, treat as normal response
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pass
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# Normal text response - format using tool-specific formatting
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formatted_content = self.format_response(raw_text, request)
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# Determine content type based on the formatted content
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content_type = (
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"markdown"
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if any(
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marker in formatted_content for marker in ["##", "**", "`", "- ", "1. "]
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)
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else "text"
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)
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return ToolOutput(
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status="success",
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content=formatted_content,
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content_type=content_type,
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metadata={"tool_name": self.name},
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)
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@abstractmethod
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async def prepare_prompt(self, request) -> str:
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"""
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Prepare the complete prompt for the Gemini model.
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This method should combine the system prompt with the user's request
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and any additional context (like file contents) needed for the task.
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Args:
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request: The validated request object
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Returns:
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str: Complete prompt ready for the model
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"""
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pass
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def format_response(self, response: str, request) -> str:
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"""
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Format the model's response for display.
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Override this method to add tool-specific formatting like headers,
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summaries, or structured output. Default implementation returns
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the response unchanged.
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Args:
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response: The raw response from the model
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request: The original request for context
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Returns:
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str: Formatted response
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"""
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return response
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def _validate_token_limit(self, text: str, context_type: str = "Context") -> None:
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"""
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Validate token limit and raise ValueError if exceeded.
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This centralizes the token limit check that was previously duplicated
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in all prepare_prompt methods across tools.
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Args:
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text: The text to check
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context_type: Description of what's being checked (for error message)
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Raises:
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ValueError: If text exceeds MAX_CONTEXT_TOKENS
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"""
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from config import MAX_CONTEXT_TOKENS
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from utils import check_token_limit
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within_limit, estimated_tokens = check_token_limit(text)
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if not within_limit:
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raise ValueError(
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f"{context_type} too large (~{estimated_tokens:,} tokens). "
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f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
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)
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def create_model(
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self, model_name: str, temperature: float, thinking_mode: str = "medium"
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):
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"""
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Create a configured Gemini model instance.
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This method handles model creation with appropriate settings including
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temperature and thinking budget configuration for models that support it.
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Args:
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model_name: Name of the Gemini model to use
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temperature: Temperature setting for response generation
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thinking_mode: Thinking depth mode (affects computational budget)
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Returns:
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Model instance configured and ready for generation
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"""
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# Map thinking modes to computational budget values
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# Higher budgets allow for more complex reasoning but increase latency
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thinking_budgets = {
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"minimal": 128, # Minimum for 2.5 Pro - fast responses
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"low": 2048, # Light reasoning tasks
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"medium": 8192, # Balanced reasoning (default)
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"high": 16384, # Complex analysis
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"max": 32768, # Maximum reasoning depth
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}
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thinking_budget = thinking_budgets.get(thinking_mode, 8192)
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# Gemini 2.5 models support thinking configuration for enhanced reasoning
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# Skip special handling in test environment to allow mocking
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if "2.5" in model_name and not os.environ.get("PYTEST_CURRENT_TEST"):
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try:
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# Retrieve API key for Gemini client creation
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api_key = os.environ.get("GEMINI_API_KEY")
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if not api_key:
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raise ValueError("GEMINI_API_KEY environment variable is required")
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client = genai.Client(api_key=api_key)
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# Create a wrapper class to provide a consistent interface
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# This abstracts the differences between API versions
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class ModelWrapper:
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def __init__(
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self, client, model_name, temperature, thinking_budget
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):
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self.client = client
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self.model_name = model_name
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self.temperature = temperature
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self.thinking_budget = thinking_budget
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def generate_content(self, prompt):
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response = self.client.models.generate_content(
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model=self.model_name,
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contents=prompt,
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config=types.GenerateContentConfig(
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temperature=self.temperature,
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candidate_count=1,
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thinking_config=types.ThinkingConfig(
|
|
thinking_budget=self.thinking_budget
|
|
),
|
|
),
|
|
)
|
|
|
|
# Wrap the response to match the expected format
|
|
# This ensures compatibility across different API versions
|
|
class ResponseWrapper:
|
|
def __init__(self, text):
|
|
self.text = text
|
|
self.candidates = [
|
|
type(
|
|
"obj",
|
|
(object,),
|
|
{
|
|
"content": type(
|
|
"obj",
|
|
(object,),
|
|
{
|
|
"parts": [
|
|
type(
|
|
"obj",
|
|
(object,),
|
|
{"text": text},
|
|
)
|
|
]
|
|
},
|
|
)(),
|
|
"finish_reason": "STOP",
|
|
},
|
|
)
|
|
]
|
|
|
|
return ResponseWrapper(response.text)
|
|
|
|
return ModelWrapper(client, model_name, temperature, thinking_budget)
|
|
|
|
except Exception:
|
|
# Fall back to regular API if thinking configuration fails
|
|
# This ensures the tool remains functional even with API changes
|
|
pass
|
|
|
|
# For models that don't support thinking configuration, use standard API
|
|
api_key = os.environ.get("GEMINI_API_KEY")
|
|
if not api_key:
|
|
raise ValueError("GEMINI_API_KEY environment variable is required")
|
|
|
|
client = genai.Client(api_key=api_key)
|
|
|
|
# Create a simple wrapper for models without thinking configuration
|
|
# This provides the same interface as the thinking-enabled wrapper
|
|
class SimpleModelWrapper:
|
|
def __init__(self, client, model_name, temperature):
|
|
self.client = client
|
|
self.model_name = model_name
|
|
self.temperature = temperature
|
|
|
|
def generate_content(self, prompt):
|
|
response = self.client.models.generate_content(
|
|
model=self.model_name,
|
|
contents=prompt,
|
|
config=types.GenerateContentConfig(
|
|
temperature=self.temperature,
|
|
candidate_count=1,
|
|
),
|
|
)
|
|
|
|
# Convert to match expected format
|
|
class ResponseWrapper:
|
|
def __init__(self, text):
|
|
self.text = text
|
|
self.candidates = [
|
|
type(
|
|
"obj",
|
|
(object,),
|
|
{
|
|
"content": type(
|
|
"obj",
|
|
(object,),
|
|
{
|
|
"parts": [
|
|
type("obj", (object,), {"text": text})
|
|
]
|
|
},
|
|
)(),
|
|
"finish_reason": "STOP",
|
|
},
|
|
)
|
|
]
|
|
|
|
return ResponseWrapper(response.text)
|
|
|
|
return SimpleModelWrapper(client, model_name, temperature)
|