""" Debug Issue tool - Root cause analysis and debugging assistance """ from typing import TYPE_CHECKING, Any, Optional from pydantic import Field if TYPE_CHECKING: from tools.models import ToolModelCategory from config import TEMPERATURE_ANALYTICAL from systemprompts import DEBUG_ISSUE_PROMPT from .base import BaseTool, ToolRequest class DebugIssueRequest(ToolRequest): """Request model for debug tool""" prompt: str = Field(..., description="Error message, symptoms, or issue description") error_context: Optional[str] = Field(None, description="Stack trace, logs, or additional error context") files: Optional[list[str]] = Field( None, description="Files or directories that might be related to the issue (must be absolute paths)", ) runtime_info: Optional[str] = Field(None, description="Environment, versions, or runtime information") previous_attempts: Optional[str] = Field(None, description="What has been tried already") class DebugIssueTool(BaseTool): """Advanced debugging and root cause analysis tool""" def get_name(self) -> str: return "debug" def get_description(self) -> str: return ( "DEBUG - Root cause analysis. Share all diagnostic files, logs, traces. " "1M token capacity. More context = better analysis." ) def get_input_schema(self) -> dict[str, Any]: schema = { "type": "object", "properties": { "prompt": { "type": "string", "description": "Error message, symptoms, or issue description", }, "model": self.get_model_field_schema(), "error_context": { "type": "string", "description": "Stack trace, logs, or additional error context", }, "files": { "type": "array", "items": {"type": "string"}, "description": "Files or directories that might be related to the issue (must be absolute paths)", }, "runtime_info": { "type": "string", "description": "Environment, versions, or runtime information", }, "previous_attempts": { "type": "string", "description": "What has been tried already", }, "temperature": { "type": "number", "description": "Temperature (0-1, default 0.2 for accuracy)", "minimum": 0, "maximum": 1, }, "thinking_mode": { "type": "string", "enum": ["minimal", "low", "medium", "high", "max"], "description": "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)", }, "use_websearch": { "type": "boolean", "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.", "default": True, }, "file_handling_mode": { "type": "string", "enum": ["embedded", "summary", "reference"], "default": "embedded", "description": "How to handle file content in responses. 'embedded' includes full content (default), 'summary' returns only summaries to save tokens, 'reference' stores files and returns IDs.", }, "continuation_id": { "type": "string", "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.", }, }, "required": ["prompt"] + (["model"] if self.is_effective_auto_mode() else []), } return schema def get_system_prompt(self) -> str: return DEBUG_ISSUE_PROMPT def get_default_temperature(self) -> float: return TEMPERATURE_ANALYTICAL # Line numbers are enabled by default from base class for precise error location def get_model_category(self) -> "ToolModelCategory": """Debug requires deep analysis and reasoning""" from tools.models import ToolModelCategory return ToolModelCategory.EXTENDED_REASONING def get_request_model(self): return DebugIssueRequest async def prepare_prompt(self, request: DebugIssueRequest) -> str: """Prepare the debugging prompt""" # Check for prompt.txt in files prompt_content, updated_files = self.handle_prompt_file(request.files) # If prompt.txt was found, use it as prompt or error_context if prompt_content: if not request.prompt or request.prompt == "": request.prompt = prompt_content else: request.error_context = prompt_content # Check user input sizes at MCP transport boundary (before adding internal content) size_check = self.check_prompt_size(request.prompt) if size_check: from tools.models import ToolOutput raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}") if request.error_context: size_check = self.check_prompt_size(request.error_context) if size_check: from tools.models import ToolOutput raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}") # Update request files list if updated_files is not None: request.files = updated_files # Build context sections context_parts = [f"=== ISSUE DESCRIPTION ===\n{request.prompt}\n=== END DESCRIPTION ==="] if request.error_context: context_parts.append(f"\n=== ERROR CONTEXT/STACK TRACE ===\n{request.error_context}\n=== END CONTEXT ===") if request.runtime_info: context_parts.append(f"\n=== RUNTIME INFORMATION ===\n{request.runtime_info}\n=== END RUNTIME ===") if request.previous_attempts: context_parts.append(f"\n=== PREVIOUS ATTEMPTS ===\n{request.previous_attempts}\n=== END ATTEMPTS ===") # Add relevant files if provided if request.files: # Use centralized file processing logic continuation_id = getattr(request, "continuation_id", None) file_content, processed_files, file_references = self._prepare_file_content_for_prompt( request.files, continuation_id, "Code" ) # Store file references for response formatting if file_references: self._store_file_references(file_references) self._actually_processed_files = processed_files if file_content: context_parts.append(f"\n=== RELEVANT CODE ===\n{file_content}\n=== END CODE ===") full_context = "\n".join(context_parts) # Check token limits self._validate_token_limit(full_context, "Context") # Add web search instruction if enabled websearch_instruction = self.get_websearch_instruction( request.use_websearch, """When debugging issues, consider if searches for these would help: - The exact error message to find known solutions - Framework-specific error codes and their meanings - Similar issues in forums, GitHub issues, or Stack Overflow - Workarounds and patches for known bugs - Version-specific issues and compatibility problems""", ) # Combine everything full_prompt = f"""{self.get_system_prompt()}{websearch_instruction} {full_context} Please debug this issue following the structured format in the system prompt. Focus on finding the root cause and providing actionable solutions.""" return full_prompt def format_response(self, response: str, request: DebugIssueRequest, model_info: Optional[dict] = None) -> str: """Format the debugging response""" # Get the friendly model name model_name = "the model" if model_info and model_info.get("model_response"): model_name = model_info["model_response"].friendly_name or "the model" return f"""{response} --- **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."""