MCP Rest Sample Server

Overview

The Mcp Rest Sample server demonstrates how an MCP (Model Context Protocol) service can be coupled with a conventional REST API using FastAPI. By exposing MCP capabilities alongside standard HTTP endpoints, it bridges the gap between stateless web services and stateful AI assistants that rely on the MCP handshake for resource discovery, tool invocation, and prompt management. This hybrid approach lets developers maintain existing RESTful infrastructure while gradually adopting AI‑centric workflows.

At its core, the server hosts an MCP instance that implements the full protocol stack: it advertises resources (e.g., datasets or models), offers tools that can be called by an AI client, and provides prompt templates for dynamic conversation contexts. Simultaneously, FastAPI routes expose the same functionality via HTTP endpoints, enabling traditional clients or scripted integrations to interact with the service. The Python SDK for MCP is used internally to serialize and deserialize protocol messages, ensuring compliance with the spec without reinventing low‑level plumbing.

Key capabilities include:

• Resource registration: Dynamically expose data or model endpoints that an AI assistant can query.
• Tool execution: Define callable functions (e.g., database lookups, calculation services) that the assistant can invoke on demand.
• Prompt templating: Store and retrieve structured prompts, allowing the assistant to inject context or constraints into its responses.
• Sampling configuration: Adjust generation parameters (temperature, top‑k) through the MCP interface.

Developers can leverage this server in a variety of scenarios. For example, an internal knowledge base can be exposed as an MCP resource while still being consumable by legacy REST clients. A data science team might expose a model inference endpoint as an MCP tool, enabling the AI assistant to fetch predictions during a conversation. In educational settings, prompt templates can be managed centrally, ensuring consistent instructional tone across multiple assistants.

Integration into AI workflows is straightforward: a Claude or other MCP‑aware assistant initiates the protocol handshake, discovers available tools and resources, and then invokes them as needed. Because the same endpoints are reachable via HTTP, automated scripts or monitoring dashboards can also interact with the service, providing a unified interface for both human and machine consumers. The combination of FastAPI’s asynchronous performance with the structured semantics of MCP makes this sample a practical reference for building scalable, AI‑ready microservices.