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How to Build an MCP Server: A Developer's Getting-Started Guide
A practical walkthrough for building a Model Context Protocol server, from choosing a transport to exposing your first tool.
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To build an MCP server, install the official SDK for your language, choose a transport (stdio for local use, Streamable HTTP for remote), define your tools with a JSON schema describing their inputs, implement the handler for each tool, and test the connection with an MCP-compatible client before deploying.
Last reviewed 2026-08-29
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1. Choose your transport before writing any tool code
stdio is the right default for a server that only needs to run on the same machine as its client — no network exposure, no auth to build. Streamable HTTP is required only if multiple clients need to share one running server instance, or the server needs to run independently of any single client's lifecycle. Switching transports later is a real rewrite of the connection layer, so decide this first.
2. Define tools with a strict input schema
- Every tool needs a name, a description the model will read to decide when to call it, and a JSON schema for its arguments.
- Write the description for the model, not for a human reader — be explicit about what the tool does and does not do, since the model decides whether to call it based on this text alone.
- Validate every argument against the schema in your handler, not just in the schema declaration — a model can and will occasionally send malformed arguments.
3. Implement handlers with the same rigor as any external-facing API
An MCP tool handler is functionally an API endpoint that an LLM calls instead of a human client. Apply the same discipline: validate inputs, constrain what the handler can actually do (a 'run shell command' tool with no allowlist is a real security liability), handle errors by returning a clear message rather than crashing the server, and log calls for debugging.
4. Test with a real client before shipping
Connect your server to an MCP-compatible client (Claude Desktop, an IDE extension) during development and exercise each tool through natural prompts, not just direct calls — this surfaces cases where the model misunderstands your tool's description or picks the wrong tool for a task, which a unit test alone won't catch.