MCP Client Example Server

The MCP Client Example showcases a minimal yet complete implementation of the Model Context Protocol (MCP) that bridges an AI assistant with external tools. By exposing a lightweight server and a matching client, the example demonstrates how developers can quickly prototype tool integrations without delving into low‑level networking or serialization details. The server runs a couple of simple tools— for body‑mass‑index calculations and , an asynchronous wrapper around a weather API—illustrating both synchronous and asynchronous tool patterns within MCP. The client, conversely, establishes a stdio‑based session, discovers available tools, and invokes them with sample arguments, producing instant, structured responses that an LLM could consume directly.

For developers working on AI‑powered applications, this server solves the recurring challenge of tool discovery and execution. Instead of hardcoding API calls into a model’s prompt or writing custom adapters for each new service, the MCP server presents a uniform interface. An AI assistant can query , receive metadata (names, parameters, descriptions), and then call any tool by name with the appropriate arguments. This decouples the assistant’s logic from backend implementation, enabling rapid iteration and safer execution: every tool is sandboxed behind a defined contract, reducing the risk of unintended side effects.

Key capabilities highlighted in the example include:

• Tool registration: The server declares tool signatures, including parameter types and descriptions, allowing clients to validate inputs before execution.
• Synchronous vs. asynchronous support: runs locally, while demonstrates async I/O, showcasing MCP’s flexibility across execution models.
• Session management via stdio: By using standard input/output streams, the example keeps deployment simple—no network configuration is required, making it ideal for local testing or Dockerized workflows.
• Inspector integration: Running the server with launches a web‑based inspector that visualizes tool calls, arguments, and responses in real time, aiding debugging and documentation.

Typical use cases for this MCP server include:

• Rapid prototyping of new AI assistants where developers need to expose domain‑specific calculations or external data feeds without building full REST APIs.
• Educational demos that illustrate how LLMs can orchestrate multiple tools, helping students understand the flow of data and control in AI‑driven systems.
• Embedded AI assistants in local applications (e.g., IDE extensions, desktop helpers) where a lightweight, stdio‑based protocol is preferable to networked services.

In practice, an AI workflow would first initialize the MCP client, query the server for available tools, and then let the assistant decide which tool to invoke based on user intent. The server’s structured responses can be fed back into the model as part of a conversational context, enabling iterative refinement or multi‑step reasoning. The combination of a clear contract, easy deployment, and built‑in inspection makes this example an excellent starting point for developers looking to add powerful tool integrations to their AI assistants.