tool-design-pattern

What problem does it solve?

This Skill provides a structured framework for designing and implementing AI tools (e.g., for LangChain, function calling). It addresses common challenges like inconsistent input schemas, poor error handling, and lack of testability, ensuring your tools are robust, predictable, and easy to integrate.

Core Features & Use Cases

• Pydantic Schema Design: Enforces clear, validated input schemas for tools using Pydantic, improving reliability and reducing runtime errors.
• Comprehensive Error Handling: Guides on implementing robust try/except blocks and structured JSON error responses for predictable tool behavior.
• Context-Aware Tools: Patterns for safely accessing and utilizing conversation context (like user_id, session_id) within tool logic.
• Async & Testing: Provides examples for building asynchronous tools and writing thorough unit tests for tool functions.
• Use Case: An LLM agent needs to interact with an external API to fetch user data. This skill helps an engineer design a fetch_user_data tool with a Pydantic input schema, proper error handling for API failures, and comprehensive tests, making the agent's interactions reliable.

Quick Start

Design a new AI tool for searching a database, ensuring it has a Pydantic input schema and robust error handling.