Build your own MCP server with Claude
Claude guides you through building an MCP server that lets an AI work with an outside service or API, in TypeScript or Python, then tests how well an AI can actually use its tools.
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AnthropicofficialMade by anthropics- 187kStars on GitHubStars
Apache-2.0License
28 OctLast update
How it works
Research and plan
Study the MCP spec, the SDK docs and the service's API, then list the endpoints to cover, most common first.
Build
Set up the project, shared API client and error handling, then each tool with input schemas, clear descriptions and annotations.
Review and test
Check for duplicated code and full type coverage, build it, and try it in the MCP Inspector.
Evaluate
Write 10 realistic, read-only questions with checkable answers, to see if an AI can solve them with your tools.
When it kicks in
- New MCP servers
- Wrapping an API
- TypeScript SDK
- Python (FastMCP)
TypeScript is the recommended stack. For a project already on the v1 TypeScript SDK, Claude mentions the v2 migration once and carries on with v1.
Before you start
- Node.js for the TypeScript SDK, or Python for FastMCP.
- For the optional evaluation script: Python with the
anthropicandmcppackages, and anANTHROPIC_API_KEY.
Try saying
Claude reads the API docs, plans the tools, builds them with schemas and read-only or destructive hints, tests the server with the MCP Inspector and drafts an evaluation file of 10 questions.
Worth knowing
The optional evaluation script runs your questions through Claude using the Anthropic API and your ANTHROPIC_API_KEY, connected to your MCP server.
Anthropic provides the skills in this repo for demonstration and education, and says to test them in your own setup before relying on them for critical work.
Comes with example-skills
This skill ships in Anthropic's example-skills plugin, so you get its other 11 skills too.
- skill-creator
- frontend-design
- webapp-testing
- web-artifacts-builder
- doc-coauthoring
- canvas-design
- and 5 more