Overview

The MCP Client‑Server example demonstrates the core architecture of a Model Context Protocol (MCP) system: a lightweight server that exposes AI‑ready tools and resources over HTTP, and a client that consumes those services. By separating the tool logic from the AI assistant’s execution environment, this pattern lets developers deploy domain‑specific capabilities on dedicated infrastructure while keeping the AI model stateless and portable. The server can be run locally for quick testing or exposed as a remote service, enabling distributed workflows where multiple assistants share the same toolset.

Problem Solved

Many AI assistants are built to be self‑contained, yet real applications often require access to external data or specialized computations. Without a clear boundary, embedding every tool directly into the assistant leads to bloated deployments and difficult maintenance. The MCP Client‑Server pattern solves this by providing a clean, HTTP‑based interface for tools, resources, and prompts. Developers can host the server on a secure machine or cloud instance, update tools independently, and scale only the compute needed for tool execution rather than the entire assistant.

Core Functionality

• Tool Exposure: The server registers functions (tools) that the assistant can invoke. Each tool is defined in a Python module and made available through a standardized MCP endpoint.
• Resource Management: Static or dynamic data assets can be served alongside tools, allowing the assistant to reference external files or datasets without embedding them in the model.
• Prompt and Sampling Support: The server can provide custom prompts or sampling strategies, enabling fine‑grained control over the assistant’s output while keeping the model lightweight.
• HTTP Integration: By listening on a configurable port, the server accepts JSON‑encoded MCP requests and returns responses in a consistent format. This makes it trivial to integrate with any language or framework that can perform HTTP calls.

Use Cases

• Enterprise Integration: A finance team can host a server that exposes APIs for real‑time market data, compliance checks, or internal policy documents. The assistant simply calls these tools via MCP, keeping the core model free of sensitive data.
• Rapid Prototyping: Developers can spin up a local MCP server, test new tools in isolation, and iterate quickly before deploying to production. The dev mode command () allows immediate feedback on tool behavior.
• Scalable Tooling: In a multi‑assistant environment, several agents can share the same MCP server. This reduces duplication of effort and ensures consistent tool behavior across deployments.

Integration with AI Workflows

An MCP‑enabled assistant first constructs a model context that includes references to the server’s tools, resources, and prompts. When the assistant decides a tool is needed, it sends an HTTP request to the server’s endpoint. The server executes the requested tool, returns the result, and the assistant incorporates that output into its next turn. This decoupled flow allows developers to update or replace tools without retraining the model, and to add new capabilities simply by deploying additional modules on the server.

Standout Advantages

• Simplicity: The example provides a minimal yet complete MCP stack, making it an ideal starting point for learning or teaching the protocol.
• Flexibility: Tools can be written in any language that exposes an HTTP API; the MCP server merely forwards requests, so developers are not locked into a single stack.
• Security: By hosting the server behind network controls, sensitive operations can be isolated from the public model endpoint.
• Modularity: Resources, prompts, and sampling strategies are all served through the same interface, enabling consistent management of auxiliary data.

In summary, the MCP Client‑Server example showcases how to build a robust, scalable bridge between AI assistants and external tooling. It empowers developers to extend model capabilities without compromising portability or maintainability, making it a practical foundation for production‑grade AI applications.