Building an MCP Client

Building an MCP client means writing the consumer side of the protocol: code that connects to a server, discovers its tools, and puts them in front of an LLM. Where a server answers the question “what can I offer?”, your client answers “how does my application use it?” A minimal client connects over a transport, calls tools/list, hands the tool definitions to a model, and executes whatever tools/call requests the model makes — the core of every MCP-powered chatbot and agent.

This matters because it’s how you make your own application extensible. Once your product embeds an MCP client, users can plug in any server — GitHub, Postgres, internal company tools — without you shipping a single integration. It’s the same mechanism Claude Desktop and Cursor use, and building one demystifies the whole stack: you’ll see exactly how tool definitions reach the model and how its responses become real function executions.

In practice you’ll follow a standard loop. Instantiate a Client from the TypeScript or Python SDK, attach a StdioClientTransport (spawning the server as a subprocess) or a streamable HTTP transport, and connect(). Convert the listed tools into the format your LLM API expects — Anthropic’s tools parameter or OpenAI’s function-calling format — then run the agent loop: send the conversation, and when the model returns a tool call, invoke callTool(), append the result, and repeat until the model produces a final answer. Wrap that in a CLI or a FastAPI endpoint and you have a working host.