Can I build my own MCP server?

Updated October 2026 · How we answer

Getting started with SDKs

Anthropic provides official SDKs for Python and TypeScript that handle the JSON-RPC communication and protocol details. You define your tools, resources, and prompts using simple decorators or functions, and the SDK takes care of the rest.

For example, in Python you might use the `mcp` package and write a function decorated with `@server.tool()` to expose it. The SDK manages the stdio or SSE transport, so you can focus on your business logic. There are also community SDKs for other languages like Go, Rust, and Java.

• Python SDK: `mcp` package, decorators for tools
• TypeScript SDK: `@modelcontextprotocol/sdk`
• Community SDKs for Go, Rust, Java, and more
• Start with a simple echo tool to test your setup

Designing your server

Decide what capabilities to expose. Tools are for actions (e.g., send a message, create a record), resources are for data (e.g., read a file, fetch a webpage), and prompts are for templates. Keep the interface focused and well-documented.

Use clear names and descriptions for each tool, because the LLM relies on them to decide when to call. Define input schemas using JSON Schema so the model knows what arguments are expected. Handle errors gracefully and return informative messages.

Consider security: if your server accesses sensitive data, implement authentication and authorization. For local servers, you can rely on the host's permissions, but for remote servers, use tokens or OAuth.

• Define tools with clear names, descriptions, and schemas
• Expose resources for read-only data access
• Use prompts for common workflows
• Implement logging and error handling

Testing and deploying

Test your server locally with an MCP-compatible host like Claude Desktop. You can configure the host to launch your server as a subprocess. Use the host's developer tools to inspect the messages exchanged.

For remote deployment, run your server behind an HTTP+SSE endpoint. You'll need to handle authentication and ensure the server can scale. Many developers package their servers as Docker containers or npm/PyPI packages for easy distribution.

Once built, you can share your server with others. The MCP community maintains a list of servers, and you can publish yours to GitHub or a registry. Building an MCP server is a great way to contribute to the AI ecosystem.

• Test with Claude Desktop or another MCP host
• Use stdio for local, HTTP+SSE for remote
• Package for easy installation (Docker, npm, PyPI)
• Share on GitHub and list in community directories