Overview

The Mcp Server Example V2 is a lightweight, FastAPI‑based implementation of the Model Context Protocol (MCP). It addresses the common developer need to expose a stable, HTTP‑based context service that an AI assistant can query for prompt templates and contextual data. By handling the boilerplate of MCP routing, health checks, and JSON payload parsing, this server lets teams focus on designing prompt logic rather than protocol plumbing.

At its core, the server offers two endpoints: a simple health‑check () that confirms the service is running, and a endpoint that accepts a JSON payload containing a and optional parameters. The server then returns the rendered prompt text, allowing AI assistants to receive fully‑formed prompts without embedding template logic inside the client. This separation of concerns is valuable for teams that want to centralize prompt maintenance, enforce versioning, or apply dynamic substitutions based on user data.

Key capabilities include:

• Parameterized prompts: Pass arbitrary key/value pairs that the server substitutes into templates, enabling personalized or context‑aware prompts.
• Template management: Store and retrieve prompt definitions by ID, making it easy to add new prompts or update existing ones without redeploying the assistant.
• Health monitoring: A dedicated health endpoint ensures that orchestration tools or deployment pipelines can verify the MCP service is healthy before routing traffic.

Typical use cases involve AI chatbots that need to generate greetings, FAQs, or domain‑specific instructions on the fly. For example, a customer support assistant can query with and parameters like the customer’s name or time of day, receiving a ready‑to‑send greeting. In data‑driven applications, the server can embed dynamic statistics or user metrics directly into prompts, keeping the assistant’s responses fresh and relevant.

Integrating this MCP server into an AI workflow is straightforward: the assistant simply sends a JSON request to and receives the rendered prompt. The server can be deployed behind an API gateway, paired with authentication middleware, or scaled horizontally to meet demand. Its minimal footprint and adherence to MCP standards make it an ideal starting point for teams looking to prototype or iterate on context services before moving to more feature‑rich production servers.