Hello MCP Server

Overview

The Hello MCP server is a lightweight, ready‑to‑run implementation of the Model Context Protocol (MCP) written in Python. It demonstrates how to expose an AI assistant’s capabilities—such as resources, tools, prompts, and sampling parameters—to external clients using the official MCP Python SDK. By providing a minimal yet fully functional server, it lowers the barrier for developers who want to experiment with MCP without having to build infrastructure from scratch.

Solving a Common Integration Problem

Developers building AI assistants often need to expose custom logic, data sources, or fine‑tuned prompts in a standardized way. Traditional approaches require bespoke APIs and custom authentication flows, leading to fragmented codebases and increased maintenance overhead. Hello MCP consolidates these concerns into a single protocol‑compliant server, allowing assistants to discover and invoke capabilities declaratively. This eliminates the need for hand‑crafted integration layers, streamlines onboarding of new tools, and ensures consistent security and versioning across the ecosystem.

What the Server Provides

• Resource Registry – A catalog of reusable data assets (e.g., knowledge bases, embeddings) that assistants can query at runtime.
• Tool Endpoints – RESTful interfaces for executing custom functions such as data retrieval, calculations, or external API calls.
• Prompt Templates – A mechanism to store and serve prompt variants, enabling dynamic prompt selection based on context.
• Sampling Controls – Fine‑grained configuration of temperature, top‑k, and other generation parameters, allowing assistants to tailor output style per request.
• Health & Discovery – Standard MCP endpoints for health checks and capability discovery, ensuring smooth integration with client tooling.

Real‑World Use Cases

• Domain‑Specific Assistants – A legal assistant can expose a “Case Law Search” tool and a repository of precedent documents, while a medical chatbot provides a symptom checker endpoint.
• Data‑Driven Workflows – A data science assistant can retrieve real‑time market data via a tool endpoint, then feed it into a prompt that generates trend analyses.
• Multi‑Modal Collaboration – By combining resource streams (e.g., image embeddings) with prompt templates, developers can build assistants that handle both text and visual inputs seamlessly.

Integration into AI Workflows

Because it adheres to the MCP specification, Hello MCP can be plugged into any client that understands MCP—Claude, Gemini, or custom in‑house assistants. Clients first discover the server’s capabilities through the endpoint, then invoke tools or fetch resources by calling the appropriate URLs. The SDK’s built‑in authentication and request validation ensure that only authorized assistants can access sensitive tools or data, making it suitable for production deployments.

Unique Advantages

• Zero Boilerplate – The server ships with a pre‑configured configuration, routing, and authentication layer; developers can focus on business logic.
• Extensibility – Built on the MCP Python SDK, it can be extended with additional tools or resources without modifying core server code.
• Community‑Supported – As an example implementation, it serves as a reference for best practices and can be forked or contributed to by the broader MCP community.

Hello MCP exemplifies how a minimal, protocol‑first approach can accelerate AI assistant development while maintaining flexibility and security across diverse use cases.