Test MCP Server

The test-mcp server provides a lightweight, ready‑to‑use implementation of the Model Context Protocol (MCP) that can be dropped into any development environment to validate and experiment with AI‑assistant integrations. By exposing a minimal set of MCP endpoints—resources, tools, prompts, and sampling—the server lets developers prototype how an external service can be queried from within a Claude or similar assistant without the overhead of building a full‑featured backend.

At its core, the server addresses the common pain point of contextual data retrieval for AI assistants. When a user asks a question that requires up‑to‑date facts, code snippets, or domain knowledge not baked into the model, an MCP server can supply that information on demand. test‑mcp demonstrates how a client can register a simple “search” tool that returns mock search results, allowing developers to test the flow of data from a user prompt through an external tool invocation and back into the assistant’s response.

Key capabilities of test‑mcp include:

• Resource discovery: Clients can list available tools and prompts, making it easy to discover what the server offers without hard‑coding URLs.
• Tool invocation: A single, well‑defined tool endpoint accepts JSON payloads and returns structured results, mirroring the pattern used by production MCP services.
• Prompt templates: The server exposes a set of reusable prompt fragments that can be combined with tool outputs, enabling developers to experiment with dynamic prompt construction.
• Sampling hooks: While basic in this test implementation, the sampling endpoint illustrates how an assistant could modify or filter generated text before returning it to the user.

Typical use cases for test‑mcp span from rapid prototyping—where a developer wants to quickly see how an assistant will behave when calling an external API—to integration testing, ensuring that a custom tool behaves correctly under the MCP contract. It is also valuable for educational purposes, allowing students to see how the protocol’s pieces fit together in a controlled environment.

Because it follows the MCP specification closely, test‑mcp integrates seamlessly into existing AI workflows. A Claude assistant can be configured to point at the server’s base URL, automatically discover the “search” tool, and invoke it when a user request contains relevant keywords. The assistant’s response can then combine the tool output with its own generation, producing a coherent answer that blends model knowledge with real‑time data. This modularity encourages developers to extend the server with additional tools—such as database queries, weather APIs, or internal knowledge bases—without rewriting the assistant logic.

In summary, test‑mcp is a pragmatic entry point for developers looking to explore the Model Context Protocol. It solves the problem of contextual data access, offers a clear set of MCP features in plain language, and provides a solid foundation for building more sophisticated AI‑assistant integrations that rely on external tools and dynamic prompts.