PathweysAI Engineer Track · Mcp
Building an MCP Client
Building an MCP Client
An MCP client is the piece that connects to a server, negotiates what that server is actually capable of, and hands its tools over to the host application to use. Building one means handling the full connection lifecycle, starting the connection, negotiating capabilities, exchanging messages, and translating whatever a server offers into the shape your particular agent framework expects.
Why this matters
The client is where MCP actually meets your application: it is the decision point for which servers get trusted, how their tools get surfaced, and how a server's data maps onto your agent's own way of calling tools. Most people use a client that is already built into their tooling, but understanding what the client role actually does matters for debugging and for reasoning about security.
Building an MCP client
A client implements the other half of the handshake a server expects: connect, call initialize, discover available tools, then call them as needed. The sequence is fixed and mandatory in order: initialize always runs first to exchange capabilities and protocol version, list_tools then discovers exactly what the connected server exposes, and call_tool actually invokes a specific tool by name with an arguments dictionary. Reading resources and fetching prompts follow this exact same basic shape, just with their own corresponding methods.
The client's lifecycle is connect, initialize, list, then call. It is the loop that turns whatever a server discovers into functions your own application can actually invoke.
Wiring MCP tools into an agent
The practical use of an MCP client is feeding the tools it discovers directly into an LLM's own function-calling loop, which requires converting MCP's tool schema into whatever shape the specific model SDK in use actually expects, name, description, and parameters mapped across cleanly. Dispatching a call in the other direction works the same way: when the model requests a specific tool call, that request gets routed back through the MCP client's call_tool method, and the resulting content gets folded back into the ongoing conversation. The integration surface this requires is genuinely narrow: discover the available tools, map their schema once, and dispatch calls through that mapping, while everything else about the agent, its loop, its retries, its guardrails, stays entirely in your own code, which is exactly how MCP is designed to be used: as a tool contract, not a full agent framework.
MCP plugs into an agent at exactly two points: mapping list_tools results into your SDK's own tool schema, and mapping call_tool results back into the conversation. That narrow integration surface is the entire point of the protocol.
Worked example
An agent built on the OpenAI SDK that connects to an MCP filesystem server maps that server's list_tools response into the function-calling format OpenAI's API expects, so the model can call read_file exactly as if it were a native OpenAI function, with the MCP client transparently handling the actual dispatch and result underneath.
Practice
Write a client that connects to your MCP server, lists its tools, and calls one, mapping the result into a standardized shape your own application can use. Produce a working call-tool round trip, along with a written note on where you would add authentication and trust decisions inside the client.
Interview checks
- Why does the client, rather than the server, function as the actual security gate in an MCP deployment?
- What specifically has to happen before a client is allowed to call a server's tools?
- Why does MCP deliberately keep the agent loop itself outside of the client's responsibility?
Common failure modes
- Trusting any server a user configures without ever prompting for explicit trust, when the client is actually the real security gate.
- Not handling capability negotiation properly, assuming a specific tool exists on a server without actually checking first.
- Mixing up transport configurations between different servers the client connects to.
- Leaking a server's raw, unsanitized result directly into the model's context with no validation at all.
Resources
- MCP: Build a client — The official client quickstart, covering the full connection and negotiation flow a client has to implement.
- MCP Python SDK — The reference client implementation; its examples show the initialize, list-tools, and call-tool flow directly.
Expert signals
Understanding that the client itself is the actual trust boundary in an MCP deployment, explicitly mapping MCP tools onto the application's own tool model rather than assuming compatibility, and handling connection and capability failures gracefully rather than assuming every server behaves identically, is what separates a client built for production from one that only ever worked against a single, well-behaved test server.
Confidence
medium