refactor: rename debug_issue tool to debug for brevity
- Rename debug_issue.py to debug.py - Update tool name from 'debug_issue' to 'debug' throughout codebase - Update all references in server.py, tests, and README - Keep DebugIssueTool class name for backward compatibility - All tests pass with the renamed tool This makes the tool name shorter and more consistent with other tool names like 'chat' and 'analyze'. The functionality remains exactly the same. 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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Fahad
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tools/debug.py
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189
tools/debug.py
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"""
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Debug Issue tool - Root cause analysis and debugging assistance
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"""
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from typing import Any, Optional
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from mcp.types import TextContent
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from pydantic import Field
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from config import TEMPERATURE_ANALYTICAL
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from prompts import DEBUG_ISSUE_PROMPT
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from utils import read_files
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from .base import BaseTool, ToolRequest
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from .models import ToolOutput
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class DebugIssueRequest(ToolRequest):
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"""Request model for debug tool"""
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error_description: 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 help tracking down bugs or understanding errors. "
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"Triggers: 'debug this', 'why is this failing', 'root cause', 'trace error', 'diagnose issue'. "
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"IMPORTANT: Share diagnostic files liberally! Gemini 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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)
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def get_input_schema(self) -> dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"error_description": {
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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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"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 (128), low (2048), medium (8192), high (16384), max (32768)",
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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": False,
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},
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},
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"required": ["error_description"],
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}
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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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def get_request_model(self):
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return DebugIssueRequest
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async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
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"""Override execute to check error_description and error_context size before processing"""
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# First validate request
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request_model = self.get_request_model()
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request = request_model(**arguments)
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# Check error_description size
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size_check = self.check_prompt_size(request.error_description)
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if size_check:
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return [TextContent(type="text", text=ToolOutput(**size_check).model_dump_json())]
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# Check error_context size if provided
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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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return [TextContent(type="text", text=ToolOutput(**size_check).model_dump_json())]
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# Continue with normal execution
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return await super().execute(arguments)
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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 error_description or error_context
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# Priority: if error_description is empty, use it there, otherwise use as error_context
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if prompt_content:
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if not request.error_description or request.error_description == "":
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request.error_description = prompt_content
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else:
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request.error_context = prompt_content
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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.error_description}\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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file_content = read_files(request.files)
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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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"""Specifically search for:
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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) -> str:
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"""Format the debugging response"""
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return f"Debug Analysis\n{'=' * 50}\n\n{response}\n\n---\n\n**Next Steps:** Evaluate Gemini's recommendations, synthesize the best fix considering potential regressions, test thoroughly, and ensure the solution doesn't introduce new issues."
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