AI Sticky Notes MCP Server: Enabling Seamless Integration of AI Applications

Overview: What is AI Sticky Notes MCP Server?

AI Sticky Notes is an implementation of an MCP (Model Context Protocol) server designed to facilitate the management and summarization of sticky notes. The core objective of this server is to provide a standardized API for different AI applications, allowing them to interact with note data seamlessly. Built using the FastMCP framework from the MCP project, it supports key operations such as adding new notes, reading all notes, fetching the latest note, and generating summary prompts.

🔧 Core Features & MCP Capabilities

AI Sticky Notes MCP Server leverages the Model Context Protocol (MCP) to enable smooth interactions between AI applications like Claude Desktop, Continue, Cursor, etc. This protocol acts as a universal adapter, ensuring that data sources and tools can be accessed through a standardized interface. The server's main components include:

1. Add Notes: Users can append new notes to the notes.txt file.
2. Read Notes: Retrieve all existing notes from the file.
3. Get Latest Note: Fetch the most recent note added to the list.
4. Summarize Notes: Generate a summary prompt based on all stored notes.

These features are implemented using the FastMCP library, providing a robust and efficient way for AI applications to manage and utilize note-based data.

Example Implementation

from fastmcp import FastMCP

# Initialize FastMCP server
server = FastMCP(config_path="pyproject.toml")

# Add new notes
server.add_note("This is the first note.")
server.add_note("This is the second note.")

# Read all notes
all_notes = server.read_all_notes()
print(all_notes)

# Get latest note
latest_note = server.get_latest_note()
print(latest_note)

MCP Protocol Flow

The Model Context Protocol (MCP) envisions a world where various AI applications can connect to specific data sources and tools through a standardized protocol. The following Mermaid diagram illustrates the flow of communication between an AI application, the MCP client, the MCP server, and the data source or tool:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Architecture & Protocol Implementation

The AI Sticky Notes MCP Server is architected to support a wide range of AI applications, ensuring compatibility and seamless integration. It employs the Model Context Protocol (MCP), which defines a set of rules and standards for how various components interact. The server's implementation adheres to these guidelines, providing a robust and flexible framework.

Getting Started with Installation

To get started with installing and running the AI Sticky Notes MCP Server, follow these steps:

1. Clone the repository:

   git clone https://github.com/crman/AI-Sticky-Notes-MCP-Server.git
   cd AI-Sticky-Notes-MCP-Server
   

2. Create and activate a virtual environment:

   python -m venv .venv
   .venv\Scripts\activate  # On Windows
   

3. Install dependencies using uv:

   uv sync
   

Running the Server

To start the MCP server using uv, run the following command:

uv run mcp install main.py

Key Use Cases in AI Workflows

AI Sticky Notes MCP Server is particularly useful in scenarios where real-time note management and summarization are critical. Here are two realistic use cases:

Case 1: Real-Time Research Note Management

In a research setting, scientists can quickly add, read, and summarize notes on ongoing projects. The server ensures that these notes can be easily accessed by different AI applications, enhancing collaboration and information sharing.

from fastmcp import FastMCP

server = FastMCP(config_path="pyproject.toml")

# Add real-time research notes
research_notes = [
    "Initial hypothesis formulation",
    "Experimental data collection results",
]

for note in research_notes:
    server.add_note(note)

# Generate summary of research progress
summary_prompt = server.generate_summary_prompt(research_notes)
print("Summary:", summary_prompt)

Case 2: Customer Support Ticket Management

In a customer support context, notes can be used to track issues and interactions with customers. The AI Sticky Notes MCP Server helps in maintaining an organized record of all interactions, which can then be summarized for better insights.

from fastmcp import FastMCP

server = FastMCP(config_path="pyproject.toml")

# Add customer support notes
support_notes = [
    "Customer reported issue with product A",
    "Resolved customer's complaint regarding shipping delays",
]

for note in support_notes:
    server.add_note(note)

# Generate summary of interactions
summary_prompt = server.generate_summary_prompt(support_notes)
print("Summary:", summary_prompt)

Integration with MCP Clients

The following table provides a compatibility matrix for the AI Sticky Notes MCP Server:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

Performance & Compatibility Matrix

The performance and compatibility of the AI Sticky Notes MCP Server are robust, supporting a wide range of AI applications. While most clients fully support resource and prompt interactions, tool access is limited to those specifically integrated.

Example Configuration

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Advanced Configuration & Security

Advanced users can customize the server by modifying configuration settings and enhancing security features. Key areas for customization include:

1. Environment Variables: Configure API keys, database connections, etc.
2. Secure Authentication: Implement additional authentication mechanisms such as OAuth or JWT tokens.
3. Throttling and Rate Limiting: Restrict the number of requests to prevent abuse.

FAQs

1. How do I integrate MCP clients with my AI application?

Integrate MCP clients by registering them in the server’s configuration and following the provided documentation.

2. Can multiple users access the same note database simultaneously?

Yes, read operations are designed to be thread-safe, allowing concurrent access.

3. What security measures are implemented in the server?

The server uses environment variables for API keys and supports basic authentication.

4. How do I update the protocol version with a new MCP release?

Review the MCP documentation and update your server code accordingly.

5. Can this server work without internet access?

Yes, it can operate locally if all required resources are stored on the same host.

Development & Contribution Guidelines

Contributions to the AI Sticky Notes MCP Server are welcome from developers around the world. To contribute:

1. Fork the repository: Go to GitHub and fork the project.
2. Create a new branch: Use git checkout -b feature-branch for new features or improvements.
3. Commit changes: Ensure your commits are descriptive.
4. Make a pull request: Submit a PR on GitHub detailing any changes made.

MCP Ecosystem & Resources

The Model Context Protocol (MCP) is part of a larger ecosystem aimed at standardizing the interaction between various AI applications and tools. Explore additional resources such as:

• MCP Documentation: Official documentation.
• MCP Forum: Connect with other developers.

Conclusion

The AI Sticky Notes MCP Server is a robust solution for anyone looking to integrate AI applications with note-based data management. By leveraging the Model Context Protocol, it ensures seamless and efficient interaction between diverse tools and clients, making it an invaluable asset in modern AI development workflows.