MCP Demo Server

Overview

The MCP Server Experiments package is a lightweight, extensible platform that demonstrates how to expose custom tool and resource capabilities to AI assistants via the Message Control Protocol (MCP). It tackles a common pain point in modern AI workflows: how to give an assistant reliable, typed access to external services without hard‑coding each integration. By running a single MCP server, developers can publish any number of tools—ranging from simple arithmetic helpers to complex database queries—and make them discoverable by any MCP‑compliant client, such as Claude or other conversational agents.

At its core, the server implements the MCP specification’s resource and tool contracts. Clients can query the endpoint to retrieve a catalog of available services, then invoke specific tools through structured requests that include typed arguments and return types. The server also supports dynamic prompt injection, allowing developers to supply context‑aware prompts that the assistant can reuse across conversations. This eliminates repetitive prompt engineering and ensures consistent behavior.

Key features include:

• Dynamic tool registration – Add or remove tools at runtime without restarting the server.
• Typed argument validation – Each tool declares its input schema, enabling clients to perform pre‑call checks and provide instant feedback on malformed requests.
• Prompt templating – Store reusable prompt fragments that can be composed with runtime data, ensuring prompts stay up‑to‑date and contextually relevant.
• Sampling control – Expose sampling parameters (temperature, top‑p, etc.) that clients can tweak on a per‑call basis to fine‑tune the assistant’s responses.

Typical use cases span from simple data retrieval (e.g., “fetch current weather”) to sophisticated business logic (e.g., “generate a quarterly report from internal metrics”). In enterprise settings, the server can act as a single source of truth for all AI‑enabled tooling, centralizing security, logging, and monitoring. For hobbyists or research labs, it provides a sandbox to experiment with new MCP extensions without the overhead of building full client integrations.

Integration is straightforward: an AI assistant first performs a resource discovery round, receives the list of available tools, and then constructs an MCP message that includes the desired tool name and arguments. The server processes the request, validates input, runs the underlying logic, and returns a typed response that the assistant can embed in its next turn. Because MCP is language‑agnostic and transport‑neutral, the same server can serve multiple assistants across different platforms, making it a versatile bridge between AI models and real‑world services.