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 - Four-Phase Development Workflow: Guides deep research of MCP specs and SDKs, implementation with Zod/Pydantic schemas, code review, and evaluation creation. - Language-Specific References: Includes detailed TypeScript (MCP SDK) and Python (FastMCP) implementation guides with project structure, tool registration patterns, and quality checklists. - Evaluation Harness: Ships Python scripts to run XML-based QA evaluations against MCP servers over stdio, SSE, or streamable HTTP transports. - Use Case: When asked to build an MCP server integrating an external API (e.g., a weather or GitHub service), this Skill walks through tool naming, input/output schemas, error handling, pagination, and generating 10 verifiable evaluation questions. ## Quick Start Ask the AI to build an MCP server for your chosen API using the mcp-builder skill, specifying TypeScript or Python and the target transport.