model-context-protocol

A custom server project built using the Model Context Protocol (MCP) in Python. This repository implements a simple AI Sticky Notes application that demonstrates the core functionality of MCP.

What We've Built

This project implements a simple but functional MCP server that acts as an AI Sticky Notes application with the following features:

• Adding Notes: Users can add new notes to a persistent storage file
• Reading Notes: Users can retrieve all stored notes
• Accessing Latest Note: A resource endpoint to get only the most recent note
• Note Summarization: A prompt generator that asks an AI to summarize all current notes

Core Components

1. FastMCP Server: The main server implementation using the MCP framework

2. Tools: Function endpoints that perform specific actions

• add_note: Adds a new note to storage
• read_notes: Retrieves all stored notes

3. Resources: Data endpoints that provide specific information

• notes://latest: Provides the most recently added note

4. Prompts: Template generators for AI interactions

• note_summary_prompt: Creates a prompt asking an AI to summarize all notes

What You Can Do With This Server

For Developers

• Extend Functionality: Add new tools, resources, or prompts to enhance the application
• Integrate with AI Models: Connect this server to LLMs to create an intelligent note-taking application
• Use as a Reference: Learn how to structure MCP applications for your own projects
• Build a UI: Create a frontend that interacts with these endpoints

For Users

• Manage Notes: Add and retrieve notes through the API
• Get AI Summaries: Use the prompt endpoint to generate summaries of your notes
• Access Latest Information: Quickly retrieve the most recent note

Getting Started

To run the server:

python main.py

The server will start and expose endpoints for adding notes, reading notes, accessing the latest note, and generating summary prompts.

Future Enhancements

Potential improvements for this project:

• Add note deletion and editing capabilities
• Implement note categorization and tagging
• Create a web interface for easier interaction
• Add authentication for multi-user support
• Integrate with external AI services for automatic note analysis