d36a85a3f3
When directories were provided to tools, only the directory path was stored in conversation history instead of the individual expanded files. This caused file filtering to incorrectly skip files in continued conversations. Changes: - Modified _prepare_file_content_for_prompt to return (content, processed_files) - Updated all tools to track actually processed files for conversation memory - Ensures directories are tracked as their expanded individual files Fixes issue where Swift directory with 46 files was not properly embedded in conversation continuations.
213 lines
9.4 KiB
Python
213 lines
9.4 KiB
Python
"""
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Debug Issue tool - Root cause analysis and debugging assistance
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"""
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from typing import TYPE_CHECKING, Any, Optional
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from pydantic import Field
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if TYPE_CHECKING:
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from tools.models import ToolModelCategory
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from config import TEMPERATURE_ANALYTICAL
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from systemprompts import DEBUG_ISSUE_PROMPT
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from .base import BaseTool, ToolRequest
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class DebugIssueRequest(ToolRequest):
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"""Request model for debug tool"""
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prompt: str = Field(..., description="Error message, symptoms, or issue description")
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error_context: Optional[str] = Field(None, description="Stack trace, logs, or additional error context")
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files: Optional[list[str]] = Field(
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None,
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description="Files or directories that might be related to the issue (must be absolute paths)",
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)
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runtime_info: Optional[str] = Field(None, description="Environment, versions, or runtime information")
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previous_attempts: Optional[str] = Field(None, description="What has been tried already")
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class DebugIssueTool(BaseTool):
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"""Advanced debugging and root cause analysis tool"""
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def get_name(self) -> str:
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return "debug"
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def get_description(self) -> str:
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return (
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"DEBUG & ROOT CAUSE ANALYSIS - Expert debugging for complex issues with 1M token capacity. "
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"Use this when you need to debug code, find out why something is failing, identify root causes, "
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"trace errors, or diagnose issues. "
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"IMPORTANT: Share diagnostic files liberally! The model can handle up to 1M tokens, so include: "
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"large log files, full stack traces, memory dumps, diagnostic outputs, multiple related files, "
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"entire modules, test results, configuration files - anything that might help debug the issue. "
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"Claude should proactively use this tool whenever debugging is needed and share comprehensive "
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"file paths rather than snippets. Include error messages, stack traces, logs, and ALL relevant "
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"code files as absolute paths. The more context, the better the debugging analysis. "
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"Choose thinking_mode based on issue complexity: 'low' for simple errors, "
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"'medium' for standard debugging (default), 'high' for complex system issues, "
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"'max' for extremely challenging bugs requiring deepest analysis. "
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"Note: If you're not currently using a top-tier model such as Opus 4 or above, these tools can provide enhanced capabilities."
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)
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def get_input_schema(self) -> dict[str, Any]:
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schema = {
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"type": "object",
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"properties": {
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"prompt": {
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"type": "string",
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"description": "Error message, symptoms, or issue description",
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},
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"model": self.get_model_field_schema(),
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"error_context": {
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"type": "string",
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"description": "Stack trace, logs, or additional error context",
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},
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"files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Files or directories that might be related to the issue (must be absolute paths)",
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},
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"runtime_info": {
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"type": "string",
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"description": "Environment, versions, or runtime information",
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},
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"previous_attempts": {
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"type": "string",
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"description": "What has been tried already",
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},
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"temperature": {
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"type": "number",
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"description": "Temperature (0-1, default 0.2 for accuracy)",
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"minimum": 0,
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"maximum": 1,
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},
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"thinking_mode": {
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"type": "string",
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"enum": ["minimal", "low", "medium", "high", "max"],
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"description": "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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"use_websearch": {
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"type": "boolean",
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"description": "Enable web search for documentation, best practices, and current information. Particularly useful for: brainstorming sessions, architectural design discussions, exploring industry best practices, working with specific frameworks/technologies, researching solutions to complex problems, or when current documentation and community insights would enhance the analysis.",
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"default": True,
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},
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"continuation_id": {
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"type": "string",
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"description": "Thread continuation ID for multi-turn conversations. Can be used to continue conversations across different tools. Only provide this if continuing a previous conversation thread.",
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},
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},
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"required": ["prompt"] + (["model"] if self.is_effective_auto_mode() else []),
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}
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return schema
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def get_system_prompt(self) -> str:
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return DEBUG_ISSUE_PROMPT
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def get_default_temperature(self) -> float:
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return TEMPERATURE_ANALYTICAL
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# Line numbers are enabled by default from base class for precise error location
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def get_model_category(self) -> "ToolModelCategory":
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"""Debug requires deep analysis and reasoning"""
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from tools.models import ToolModelCategory
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return ToolModelCategory.EXTENDED_REASONING
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def get_request_model(self):
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return DebugIssueRequest
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async def prepare_prompt(self, request: DebugIssueRequest) -> str:
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"""Prepare the debugging prompt"""
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# Check for prompt.txt in files
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prompt_content, updated_files = self.handle_prompt_file(request.files)
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# If prompt.txt was found, use it as prompt or error_context
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if prompt_content:
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if not request.prompt or request.prompt == "":
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request.prompt = prompt_content
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else:
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request.error_context = prompt_content
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# Check user input sizes at MCP transport boundary (before adding internal content)
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size_check = self.check_prompt_size(request.prompt)
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if size_check:
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from tools.models import ToolOutput
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raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}")
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if request.error_context:
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size_check = self.check_prompt_size(request.error_context)
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if size_check:
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from tools.models import ToolOutput
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raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}")
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# Update request files list
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if updated_files is not None:
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request.files = updated_files
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# Build context sections
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context_parts = [f"=== ISSUE DESCRIPTION ===\n{request.prompt}\n=== END DESCRIPTION ==="]
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if request.error_context:
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context_parts.append(f"\n=== ERROR CONTEXT/STACK TRACE ===\n{request.error_context}\n=== END CONTEXT ===")
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if request.runtime_info:
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context_parts.append(f"\n=== RUNTIME INFORMATION ===\n{request.runtime_info}\n=== END RUNTIME ===")
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if request.previous_attempts:
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context_parts.append(f"\n=== PREVIOUS ATTEMPTS ===\n{request.previous_attempts}\n=== END ATTEMPTS ===")
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# Add relevant files if provided
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if request.files:
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# Use centralized file processing logic
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continuation_id = getattr(request, "continuation_id", None)
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file_content, processed_files = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
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self._actually_processed_files = processed_files
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if file_content:
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context_parts.append(f"\n=== RELEVANT CODE ===\n{file_content}\n=== END CODE ===")
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full_context = "\n".join(context_parts)
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# Check token limits
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self._validate_token_limit(full_context, "Context")
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# Add web search instruction if enabled
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websearch_instruction = self.get_websearch_instruction(
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request.use_websearch,
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"""When debugging issues, consider if searches for these would help:
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- The exact error message to find known solutions
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- Framework-specific error codes and their meanings
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- Similar issues in forums, GitHub issues, or Stack Overflow
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- Workarounds and patches for known bugs
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- Version-specific issues and compatibility problems""",
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)
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# Combine everything
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full_prompt = f"""{self.get_system_prompt()}{websearch_instruction}
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{full_context}
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Please debug this issue following the structured format in the system prompt.
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Focus on finding the root cause and providing actionable solutions."""
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return full_prompt
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def format_response(self, response: str, request: DebugIssueRequest, model_info: Optional[dict] = None) -> str:
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"""Format the debugging response"""
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# Get the friendly model name
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model_name = "the model"
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if model_info and model_info.get("model_response"):
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model_name = model_info["model_response"].friendly_name or "the model"
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return f"""{response}
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---
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**Next Steps:** Evaluate {model_name}'s recommendations, synthesize the best fix considering potential regressions, and if the root cause has been clearly identified, proceed with implementing the potential fixes."""
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