Python MCP Demo Server

MCP Server Demo in Python

The MCP Server Demo for Python offers a lightweight, ready‑to‑run implementation of the Model Communication Protocol (MCP). It addresses the common challenge developers face when integrating AI assistants—namely, creating a reliable, network‑exposed endpoint that can expose custom tools, resources, and prompts to an assistant like Claude. By running on a familiar web stack (Uvicorn with FastAPI) and supporting the standard Server‑Sent Events (SSE) transport, this server removes the boilerplate of setting up a persistent, bi‑directional channel between an AI client and external services.

At its core, the server exposes a minimal API that can be extended with arbitrary functions. The bundled example includes simple arithmetic helpers (, ) and a greeting endpoint, demonstrating how developers can expose domain logic as callable tools. The SSE transport ensures that the server can stream responses in real time, which is essential for long‑running or streaming AI operations. The design follows the MCP specification closely, meaning that any client already understanding MCP can connect without additional adapters.

Key capabilities include:

• Transport flexibility: The server defaults to for local testing but can be switched to to make it reachable over a network, facilitating remote AI workflows.
• Extensibility: Developers can add new endpoints or modify existing ones, then expose them via the configuration file to any MCP‑aware tool (e.g., Cursor, Claude).
• Testability: A comprehensive suite validates both the business logic and the HTTP interface, giving confidence that new features behave as expected before deployment.
• Portability: Built on standard Python tooling (uvicorn, FastAPI), the server can run on any platform that supports Python 3.8+, making it suitable for local dev, CI pipelines, or cloud deployments.

Typical use cases include:

• Rapid prototyping: Quickly spin up a tool that an AI assistant can call during a conversation, then iterate on the logic without touching the assistant’s codebase.
• Micro‑service integration: Expose existing internal services (databases, ML models, external APIs) as MCP tools, allowing an assistant to orchestrate them on demand.
• Testing AI workflows: Use the server as a mock backend to validate how an assistant handles tool calls, error scenarios, and streaming responses before integrating with production services.

By providing a minimal yet fully compliant MCP server, this project enables developers to focus on the business logic of their tools while leveraging the powerful conversational capabilities of AI assistants.