analyze_task_complexity
Analyzes task descriptions to recommend efficient tools for execution, helping users select appropriate strategies for their specific needs.
Instructions
Analyzes a task to recommend the most efficient tool (The Router).
Args:
task_description: The user's prompt or task.Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| task_description | Yes |
Implementation Reference
- The primary handler function for the 'analyze_task_complexity' tool. It is decorated with @mcp.tool() for automatic registration with the FastMCP server. The function validates input using TaskComplexityInput schema, analyzes keywords in the task description, and returns a recommendation for strategy, complexity, tool, and reasoning.
@mcp.tool() def analyze_task_complexity(task_description: str) -> dict: """ Analyzes a task to recommend the most efficient tool (The Router). Args: task_description: The user's prompt or task. """ try: model = TaskComplexityInput(task_description=task_description) except ValidationError as e: return {"error": str(e)} task = model.task_description.lower() # Strategy: Constructor Mode (Build/Design) if any( w in task for w in [ "build", "create", "design", "architect", "system", "bot", "assistant", ] ): return { "strategy": "constructor", "complexity": "Variable", "recommended_tool": "design_context_architecture", "reasoning": "User wants to build a system/agent. Use the Architect to design a blueprint.", } # Strategy: YOLO Mode (Direct Solve) if any(w in task for w in ["project", "repo", "codebase", "architecture"]): return { "strategy": "yolo", "complexity": "Medium", "recommended_tool": "project.explore", "reasoning": "Task involves project-level understanding.", } elif any(w in task for w in ["test", "tdd", "verify"]): return { "strategy": "yolo", "complexity": "High", "recommended_tool": "workflow.test_driven", "reasoning": "Task involves testing or verification workflows.", } elif any(w in task for w in ["analyze", "reason", "think", "solve", "complex"]): return { "strategy": "yolo", "complexity": "High", "recommended_tool": "reasoning.systematic", "reasoning": "Task requires structured reasoning.", } else: return { "strategy": "yolo", "complexity": "Low", "recommended_tool": "Standard Molecule", "reasoning": "Task appears simple. Use a basic prompt or few-shot molecule.", } - Pydantic BaseModel schema used for input validation in the analyze_task_complexity tool handler.
class TaskComplexityInput(BaseModel): task_description: str = Field( ..., min_length=5, description="The user's prompt or task." )