mcp-server-on-raspi MCP Server: Universal Adapter for AI Applications

Overview: What is MCP Server?

mcp-server-on-raspi is an open-source project that implements a Model Context Protocol (MCP) server on Raspberry Pi hardware. This server acts as a bridge, facilitating the connection between advanced AI applications and specific data sources or tools through a standardized protocol. By adhering to MCP’s universal adapter model, this server ensures seamless interoperability with various AI clients such as Claude Desktop, Continue, Cursor, and others.

🔧 Core Features & MCP Capabilities

mcp-server-on-raspi introduces several core features that enable powerful integration with AI applications:

1. Custom URI Scheme: The server supports a note:// URI scheme for accessing individual notes, each with properties like name, description, and text/plain mimetype.
2. Summarize Notes Prompt: Provides a summarize-notes prompt to create summaries of stored notes, offering an option to configure the detail level (brief or detailed).
3. Add Note Tool: Allows users to add new notes by specifying a required "name" and "content". This tool updates server state and notifies clients about resource changes.

⚙️ MCP Architecture & Protocol Implementation

The architecture of mcp-server-on-raspi is designed around the Model Context Protocol, ensuring it adheres to universal standards. The protocol implementation involves handling prompts and tools through standardized interactions over stdio streams. This design ensures that the server can be easily integrated into a diverse range of AI environments without requiring modifications.

MCP Protocol Flow Diagram

The following Mermaid diagram illustrates the flow of interactions between an AI application (MCP Client) and mcp-server-on-raspi:

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

MCP Client Compatibility Matrix

mcp-server-on-raspi is fully compatible with several MCP clients, enhancing their functionality through seamless data exchange:

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

🚀 Getting Started with Installation

Install Configuration

mcp-server-on-raspi is configured through the claude_desktop_config.json file, which can be edited to add or modify server entries.

For Claude Desktop on MacOS:

"mcpServers": {
  "mcp-server-on-raspi": {
    "command": "uv",
    "args": ["--directory", "/Users/daikiwatanabe/ghq/github.com/daikw/mcp-server-on-raspi", "run", "mcp-server-on-raspi"]
  }
}

For Claude Desktop on Windows:

"mcpServers": {
  "mcp-server-on-raspi": {
    "command": "uvx",
    "args": ["mcp-server-on-raspi"]
  }
}

💡 Key Use Cases in AI Workflows

1. Note Management for Content Creators: Authors can use mcp-server-on-raspi to manage notes, which can be integrated into writing applications like Claude or Continue.
2. Real-time Data Synchronization: The server enables real-time updates of information between the note-taking tool and other AI-driven tools, improving overall productivity.

Real-world Workflow Implementations

Case 1: Content Creation with Note Management

Integration of mcp-server-on-raspi into a content creation process allows writers to seamlessly manage their notes in Claude Desktop. The server provides real-time updates, making it easier to organize and reference ideas during the writing process.

npx @modelcontextprotocol/server-notes run --directory /path/to/notes 

Case 2: Dynamic Summaries for Researchers

Researchers can use summaries generated by mcp-server-on-raspi in combination with Cursor to consolidate information quickly. By configuring the summarize-notes prompt, researchers can efficiently review and collate data.

🔌 Integration with MCP Clients

mcp-server-on-raspi supports seamless integration with various clients via custom commands and environment variables. This flexibility ensures that developers can easily adapt the server to fit their specific use cases.

Configuration Sample

The server configuration is set up using a JSON snippet:

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

📊 Performance & Compatibility Matrix

The server is optimized for performance on Raspberry Pi hardware, providing reliable service for lightweight and resource-constrained environments.

• Compatibility: Designed to work with various MCP clients across different operating systems.
• Performance: Optimized for real-time data synchronization and prompt processing.

Detailed Configuration Code Sample

The following snippet highlights the configuration details:

{
  "mcpServers": {
    "mcp-server-on-raspi": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/daikiwatanabe/ghq/github.com/daikw/mcp-server-on-raspi",
        "run",
        "mcp-server-on-raspi"
      ]
    }
  }
}

🛠️ Advanced Configuration & Security

Advanced users can customize the server by setting environment variables and adjusting command-line arguments. This flexibility allows for secure and tailored configurations.

1. Environment Variables: Set API keys, debug flags, etc.
2. Command-Line Arguments: Customize startup scripts to meet specific needs.

❓ Frequently Asked Questions (FAQ)

Q1: How does mcp-server-on-raspi enhance AI application integration?

A1: By implementing MCP protocol standards, the server enables seamless data exchange between AI applications and specific tools or data sources, improving functionality and usability.

Q2: Can the server be used with Continue and Cursor as well?

A2: Yes, but full compatibility is only supported for Claude Desktop. Users of Continue and Cursor can leverage some resources and tools while prompts may not work fully.

Q3: How do I set up logging in the server?

A3: By default, the server outputs logs to stdio. You can modify the args command to include custom flags like --log-level=debug.

Q4: Is it easy to upgrade the server with new tools or resources?

A4: Yes, by updating the configuration files and running the appropriate commands, developers can easily add new tools and resources.

Q5: What security measures are in place for sensitive API keys?

A5: Use environment variables to store API keys securely. Avoid hardcoding these values in your scripts for added security.

👨‍💻 Development & Contribution Guidelines

Contributions from the community are highly encouraged! To contribute, developers can:

1. Fork the Repository: Clone down the project and make changes.
2. Create Pull Requests: Share new features or bug fixes with the community.
3. Update Documentation: Enhance documentation for better usability.

🌐 MCP Ecosystem & Resources

The Model Context Protocol (MCP) ecosystem includes a variety of servers, clients, and tools designed to enhance AI integration across different applications. Explore resources like the official MCP documentation and community forums to stay updated on the latest developments.

By integrating mcp-server-on-raspi into your development workflow, you can unlock powerful capabilities for managing data and tools in an AI-driven environment.