What problem does it solve? Building MCP (Model Context Protocol) servers that LLMs can actually use effectively requires careful tool design, schema validation, transport selection, and evaluation—this Skill provides a structured four-phase workflow covering research, implementation, review, and evaluation. ## Core Features & Use Cases - End-to-End MCP Development Workflow: Walks through research, planning, implementation, testing, and evaluation phases for MCP servers in Python (FastMCP) or TypeScript (MCP SDK). - Language-Specific Reference Guides: Includes detailed implementation guides covering Zod/Pydantic schemas, tool registration, pagination, error handling, and transport configuration (stdio vs streamable HTTP). - Evaluation Harness: Ships Python scripts to create XML-based evaluation question sets and run automated LLM-based tests against your MCP server with accuracy reports. - Use Case: You want to expose the GitHub API to an LLM agent. Use this Skill to design well-named tools with proper annotations, implement them in TypeScript, then generate 10 complex evaluation questions and measure how well an LLM answers them using only your server. ## Quick Start Ask the AI to help you build an MCP server for your chosen API, specifying whether you want Python or TypeScript.
