MCP Builder
Guide for creating high-quality MCP servers to integrate external APIs and services
WhatIsIt
A comprehensive guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. MCP servers provide tools that allow LLMs to access external services and APIs. This skill covers both Python (FastMCP) and Node/TypeScript (MCP SDK) implementations, focusing on agent-centric design principles and evaluation-driven development.
HowToUse
Development follows a structured four-phase process:
Phase 1: Deep Research and Planning
- Study agent-centric design principles (build for workflows, not just API endpoints)
- Fetch and study MCP protocol documentation
- Load framework documentation (Python SDK or TypeScript SDK)
- Exhaustively study target API documentation
- Create comprehensive implementation plan
Phase 2: Implementation
- Set up proper project structure
- Implement core infrastructure (API helpers, error handling, formatting)
- Implement tools systematically with proper input validation (Pydantic/Zod)
- Follow language-specific best practices
- Add tool annotations (readOnlyHint, destructiveHint, etc.)
Phase 3: Review and Refine
- Review code quality (DRY principle, composability, consistency)
- Test and build (avoiding main process hangs)
- Use quality checklists from language guides
Phase 4: Create Evaluations
- Write 10 complex, realistic evaluation questions
- Ensure questions are independent, read-only, and verifiable
- Create XML evaluation file for testing
KeyFeatures
- Agent-centric design principles - Build tools for complete workflows, not just API wrappers
- Context optimization - Return high-signal information, avoid data dumps
- Actionable error messages - Guide agents toward correct usage
- Support for both Python (FastMCP) and TypeScript (MCP SDK)
- Comprehensive documentation library with protocol specs and best practices
- Input validation with Pydantic v2 (Python) or Zod (TypeScript)
- Evaluation harness for testing LLM effectiveness with your tools
- Pagination, filtering, and character limit strategies
- Quality checklists ensuring production-ready code
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