Builds high-quality Model Context Protocol (MCP) servers for seamless integration between LLMs and external APIs.
About
This skill provides a comprehensive, four-phase framework for developing robust MCP servers using Python or TypeScript. It guides users through agent-centric design principles, implementation of workflow-optimized tools, and rigorous evaluation methodologies. By focusing on high-signal data transfer, actionable error messages, and context window efficiency, it ensures that AI agents can effectively interact with external services while maintaining operational safety and reliability.
Key Features
Comprehensive error handling and natural language feedback systems
Evaluation-driven development guides for testing server effectiveness
Agent-centric design patterns focusing on workflows over raw API endpoints
Step-by-step implementation workflows for Python (FastMCP) and TypeScript (MCP SDK)
Tool optimization strategies for managing limited LLM context windows
25 GitHub stars
Use Cases
Building custom connectors for third-party SaaS platforms like Slack, GitHub, or Jira
Standardizing the development of tool-calling capabilities across developer teams
Integrating internal company APIs with Claude for automated backend workflows
