mcp-builder

To build an MCP server for LLM integration, follow an end-to-end development guide covering research, planning, implementation, and evaluation phases. Use language-specific best practices for Python (FastMCP) or Node/TypeScript to create robust, agent-centric tools.

Best practices for MCP tool development in Python involve using the FastMCP framework to design actionable, context-aware tools. Focus on agent-centric design principles, tool optimization for limited context, and actionable error messages to ensure effective LLM interaction.

Yes, MCP server implementation supports both Python and TypeScript. The development guide provides detailed, language-specific best practices for Python using FastMCP, alongside specific guidance for Node and TypeScript environments to ensure high-quality server creation.

To evaluate MCP server effectiveness for LLMs, use an evaluation-driven development framework. This involves creating rigorous evaluations designed to test how well the server's tools perform during agent interactions and managing external API integrations.

The first step in MCP server implementation is Phase 1: Deep Research and Planning. This phase guides you through researching external service APIs and designing context-aware tools before writing any code for the LLM integration.

LLM tools fail with limited context when they lack agent-centric design and tool optimization. To fix this, implement actionable error messages and context-aware tool design principles during the planning and implementation phases of your MCP server.