Test Server MCP

Overview

The Jtorreggiani Test Python MCP Server is a lightweight, example implementation of the Model Context Protocol (MCP) that demonstrates how an AI assistant can interact with a simple note‑taking backend. By exposing a minimal set of resources, prompts, and tools, it shows developers how to extend an AI workflow with persistent data storage without needing a full‑blown database or complex infrastructure.

What problem does it solve?

Modern AI assistants are powerful, but they often lack a way to persist state across sessions or share structured data with external services. This server bridges that gap by providing a first‑class, MCP‑compatible note storage system. Developers can use the server to store, retrieve, and manipulate user notes directly from within an AI conversation, enabling richer interactions such as summarization, organization, or collaborative editing.

Core functionality and value

• Resource management: Notes are exposed as URIs, each with a clear name, description, and plain‑text content. The server handles CRUD operations automatically, allowing the AI to fetch or list notes as needed.
• Prompt generation: The prompt aggregates all stored notes into a single, coherent summary. An optional argument lets the assistant tailor the level of detail—brief or detailed—based on user preference.
• Tool execution: The tool lets the assistant add new entries on demand. It accepts a note name and content, updates the server state, and broadcasts changes to any connected clients. This real‑time notification model keeps multiple assistants or interfaces in sync.

These features provide a clear, hands‑on example of how MCP can turn an AI assistant into a collaborative workspace where data is not only read but also created and updated through natural language commands.

Use cases in practice

• Personal knowledge management: Users can dictate new notes and ask the assistant to summarize them, turning a conversation into an organized knowledge base.
• Team collaboration: Multiple team members can add or modify notes via the assistant, with all changes instantly reflected across devices.
• Educational tools: Instructors can collect student reflections or questions, then generate concise summaries for quick review.

Integration with AI workflows

Because the server follows MCP standards, any Claude or similar client that supports the protocol can discover its resources, prompts, and tools automatically. Developers simply add a configuration entry to their client, and the assistant can invoke or request a prompt without additional code. The server’s stateless design over stdio also means it can be deployed in a variety of environments—from local development machines to cloud functions—without needing persistent connections.

Unique advantages

• Simplicity: With only one resource type and two tools, the server is easy to understand, modify, or extend.
• Python‑friendly: Built using modern Python tooling (, ), it demonstrates best practices for packaging and publishing MCP servers.
• Debugging support: The included guidance on using the MCP Inspector makes troubleshooting straightforward, a common pain point for developers new to MCP.

In summary, this test server is not just an example; it’s a practical template that shows how to turn an AI assistant into a dynamic, data‑aware collaborator with minimal effort.