MCP Learning Project

Overview

The MCP 学习项目 is a hands‑on learning platform that demonstrates how to build an MCP (Model Context Protocol) server capable of exposing a simple arithmetic toolkit. By combining FastAPI, Uvicorn, and the MCP‑SSE transport, it offers a lightweight yet complete example of how an AI assistant can invoke external services through streaming or standard I/O channels. The project addresses a common developer pain point: integrating legacy or domain‑specific logic into modern AI workflows without reinventing the wheel.

What Problem Does It Solve?

Developers often need to expose existing backend functionality—such as a Java microservice or legacy API—to AI assistants that communicate via MCP. However, setting up the correct transport layer, handling type validation, and managing asynchronous calls can be tedious. This server solves that by providing a ready‑made MCP implementation that:

• Wraps a straightforward arithmetic API.
• Supports both stdio (local command execution) and SSE (remote streaming) transports.
• Handles type safety, timeouts, and auto‑approval of tool names out of the box.

Core Functionality & Value

The server implements a four‑function calculator (add, subtract, multiply, divide) that is exposed as MCP tools. Its key features include:

• Dual Transport Modes – developers can choose between a local stdio process or an SSE‑based remote service, depending on deployment constraints.
• Real‑Time Streaming – the SSE channel sends progress updates and results back to the AI client, enabling more interactive conversations.
• Strong Typing – input validation is enforced through Pydantic models, reducing runtime errors and improving developer confidence.
• Asynchronous HTTP Client – the server can call downstream services (e.g., an API server) without blocking, maintaining high throughput.

These capabilities make the MCP server a valuable template for any scenario where an AI assistant must interact with deterministic, stateless functions that can be exposed over HTTP or local processes.

Use Cases & Real‑World Scenarios

1. Legacy System Integration – Wrap existing business logic (e.g., a Java microservice) in an MCP interface so AI assistants can query it as if it were a native tool.
2. Rapid Prototyping – Quickly spin up an AI‑ready service for demo purposes, using the arithmetic toolkit as a placeholder until real logic is implemented.
3. Educational Projects – Teach students or new developers how MCP transports work by experimenting with stdio vs. SSE modes.
4. Hybrid Deployments – Run the server locally during development (stdio) and switch to a remote SSE endpoint in production, demonstrating flexibility.

Integration with AI Workflows

The MCP server plugs directly into any AI client that understands the Model Context Protocol. By configuring a cline (or similar MCP‑compatible tool) with the provided JSON snippets, developers can:

• Register the server’s tools (, , etc.) for auto‑approval.
• Set timeouts and transport types, ensuring the assistant can invoke calculations without latency surprises.
• Receive streaming responses that allow the AI to update users in real time, improving transparency and user experience.

Because the server communicates over standard HTTP/SSE, it can be deployed behind existing API gateways or load balancers without special networking requirements.

Unique Advantages

• Microservice‑Ready Architecture – The diagram shows a clean separation between the MCP server, an HTTP RPC API server, and the underlying calculation functions, mirroring real production setups.
• Minimal Dependencies – Built on Starlette and Uvicorn, the server stays lightweight while still offering robust async support.
• Extensibility – Adding new tools is a matter of defining a function and exposing it via the same MCP interface; no transport changes are needed.
• Built‑in Testing – The accompanying pytest suite demonstrates how to unit test the MCP endpoints, encouraging good testing practices.

Overall, this MCP server study provides a concise, practical reference for developers looking to expose deterministic services to AI assistants with minimal friction.