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mcp-builder

Build MCP servers in Python and TypeScript with tool interfaces and validation.

167k|19.9k|Updated Sep 22, 2025
One-click install
npx skills add https://github.com/anthropics/skills --skill mcp-builder-anthropics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/anthropics/skills/tree/main/skills/mcp-builder
Command: npx skills add https://github.com/anthropics/skills --skill mcp-builder-anthropics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the complexity of building MCP servers, enabling you to create AI agents that can interact with any API or external system.

Core Features & Use Cases

  • Comprehensive Development Guide: Step-by-step process from planning to evaluation.
  • Multi-Language Support: Complete patterns for TypeScript and Python implementations.
  • Use Case: Imagine you need to connect Claude to your company's CRM, project management tools, or custom APIs - this Skill provides the complete blueprint.

Quick Start

Use the mcp-builder skill to create a new MCP server for the GitHub API that allows AI agents to search repositories, create issues, and manage pull requests.

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 connect Claude to external APIs?▼

Build MCP servers using FastMCP (Python) or the MCP SDK (TypeScript/Node) to create tool interfaces that let Claude interact with any external service. Define structured schemas for inputs and outputs, register tools with consistent naming, and implement error handling to ensure reliable agent automation.

What's the difference between TypeScript and Python for building MCP servers?▼

Both TypeScript and Python support MCP server development through dedicated SDKs. Choose based on your existing stack: the MCP SDK for TypeScript/Node projects, FastMCP for Python. Both follow the same best practices for tool registration, validation, and error handling.

Do I need to handle pagination and token limits when building MCP servers?▼

Yes. MCP best practices require token-aware design, pagination support for large result sets, and consistent error handling. These patterns ensure your MCP server integrates efficiently with LLM workflows and evaluation pipelines without exceeding context limits.

Can I use MCP servers to automate interactions with my company's internal APIs?▼

Yes. MCP servers enable AI agents to interact with any API or external system, including internal tools like CRMs, project management platforms, and custom APIs. Design tool interfaces with clear input schemas, validation, and security controls.

What input validation and security considerations apply to MCP servers?▼

MCP servers require structured input/output schemas, input validation at the tool level, and robust error handling. Follow MCP best practices to prevent invalid requests, enforce consistent naming conventions, and secure access to external services.

How do I evaluate an MCP server before deployment?▼

MCP best practices include evaluation-ready workflows with multi-format response support, consistent naming conventions, and comprehensive error handling. Test tool registration patterns, validate schemas, and ensure your server meets requirements before connecting to production agents.

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