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mcp-builder

Build MCP servers integrating external APIs with TypeScript or Python.

55|15|Updated Dec 1, 2025
Search Tags:#typescript#serverless#python#mcp#api integration#llm development#tools
One-click install
npx skills add https://github.com/LangConfig/langconfig --skill mcp-builder-langconfig
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/LangConfig/langconfig/tree/main/backend/skills/builtin/mcp-builder
Command: npx skills add https://github.com/LangConfig/langconfig --skill mcp-builder-langconfig

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires zod, pydantic.

What problem does it solve?

Integrating external APIs into AI agents often requires complex boilerplate, careful error handling, and robust tool design, making it challenging to extend agent capabilities. This Skill provides a comprehensive guide for creating Model Context Protocol (MCP) servers.

Core Features & Use Cases

  • API Integration: Learn to analyze external API documentation and design effective tools for LLM interaction.
  • Tool Definition: Define tools using Zod (TypeScript) or Pydantic (Python) schemas for clear input/output validation.
  • Error & Pagination Handling: Implement robust error handling and pagination helpers for reliable API interactions.
  • Use Case: You need your AI agent to interact with the GitHub API to manage issues. Use this Skill to build an MCP server that exposes tools like list_repos, get_issue, and create_comment, handling authentication and rate limits, allowing your agent to automate GitHub tasks.

Quick Start

Help me build an MCP server for the GitHub API to list repositories and get issue details.

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 to integrate external APIs with LLMs?▼

Build an MCP server by defining API client tools using Zod (TypeScript) or Pydantic (Python) schemas for input/output validation, implementing error handling and pagination helpers, then annotating tools with MCP metadata. This enables LLMs to call external APIs reliably through your agent.

What's the best way to design tool definitions for API integration?▼

Define tools using Zod or Pydantic schemas to enforce strict input/output validation, include clear descriptions for LLM context, and structure responses for reliable parsing. Schema-driven tool design prevents malformed API calls and ensures consistent agent behavior.

How do I handle errors and pagination when building API tools for agents?▼

Implement pagination utilities to fetch large result sets incrementally and add error handling that catches API failures, rate limits, and validation errors, returning structured error messages. Robust handling prevents agent failures and enables graceful fallbacks.

Can I use MCP servers with both TypeScript and Python projects?▼

Yes, MCP servers support both TypeScript and Python implementations. Use Zod for TypeScript schema validation and Pydantic for Python, applying the same API integration and tool-definition patterns across languages.

Do I need authentication handling in an MCP server for API integration?▼

Yes, MCP servers must handle authentication for external APIs—manage credentials securely, apply authentication headers or tokens, and implement rate-limit logic. Proper auth handling ensures reliable agent access to protected API resources.

What testing and validation steps are required for MCP tool definitions?▼

Validate tool schemas using Zod or Pydantic validators, test error paths, verify pagination logic, and confirm schema compliance before deployment. Testing catches schema mismatches and ensures tools behave correctly under agent execution.

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