mcp-builder

Guide MCP server development for LLM-external service integration with Python and TypeScript.

Updated Feb 14, 2026

Search Tags:#typescript #python #api development #mcp #server development #llm integration

Ready to run

npx skills add https://github.com/toanalien/ezdevsecops --skill mcp-builder-toanalien

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/toanalien/ezdevsecops/tree/main/.opencode/skills/mcp-builder
Command: npx skills add https://github.com/toanalien/ezdevsecops --skill mcp-builder-toanalien

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anthropic, mcp, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide and framework for developing robust MCP (Model Context Protocol) servers, enabling seamless integration of LLMs with external services and APIs.

Core Features & Use Cases

End-to-End Development Guide: Covers research, planning, implementation, review, and evaluation phases.
Language-Specific Best Practices: Detailed guidance for Python (FastMCP) and Node/TypeScript.
Tool Design Principles: Focuses on building effective tools for AI agents, optimizing for context, and designing actionable errors.
Evaluation Framework: Includes a guide and script for creating and running evaluations to test server effectiveness.
Use Case: Develop an MCP server that allows an LLM to interact with a project management tool's API, enabling it to create tasks, update statuses, and retrieve project details.

Quick Start

Follow the Python implementation guide to create a new MCP server.

Frequently Asked Questions about mcp-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build an MCP server for LLM and external API integration?▼

To build an MCP server for LLM integration, you follow a structured guide covering research, implementation, review, and evaluation phases. It provides best practices for designing agent-centric tools and integrating external APIs.

What is the best way to design MCP tools for AI agents?▼

The best way to design MCP tools for AI agents is to optimize for context efficiency and provide actionable error messages. This approach ensures the LLM can effectively interact with external services without wasting context window space.

Does MCP server development support both Python and TypeScript?▼

Yes, MCP server development supports both Python using FastMCP and Node.js with TypeScript. The framework provides detailed language-specific best practices for both environments to ensure robust server implementation.

How do I evaluate MCP server effectiveness for LLM interactions?▼

You evaluate MCP server effectiveness by using a dedicated evaluation guide and script included in the framework. This allows you to create and run tests to verify the server handles tool design and API integration correctly.

Can I use this framework to connect an LLM to a project management API?▼

Yes, you can use this framework to connect an LLM to a project management API. It specifically supports use cases like creating tasks, updating statuses, and retrieving project details through resource provider development.