GNURadio MCP Server Integration

What does “GNURadio” MCP Server do?

The GNURadio MCP Server is a modern, extensible Machine Control Protocol (MCP) server for GNURadio, designed to enable programmatic, automated, and AI-driven creation of GNURadio flowgraphs. This server allows integration with Large Language Models (LLMs), automation frameworks, and custom clients, empowering users to generate .grc files and control SDR (Software Defined Radio) workflows at scale. By exposing a robust MCP API, the GNURadio MCP Server streamlines SDR prototyping, allowing users to automate flowgraph creation, modification, and validation, thereby reducing manual effort and potential errors. Its modular architecture further supports easy extension and customization, making it a valuable tool for developers and researchers working with GNURadio.

List of Prompts

No prompt templates are documented in the available files or README.

List of Resources

No specific MCP resources are listed in the documentation or repository files.

List of Tools

No explicit tool list is provided in the README or main.py. The server enables flowgraph creation and manipulation, but tool details are not outlined.

Use Cases of this MCP Server

• Automated SDR Workflow Creation
  Developers can programmatically generate and modify GNURadio flowgraphs, automating repetitive SDR prototyping tasks.
• AI-Driven Flowgraph Generation
  Integrate with LLMs and automation frameworks to enable AI-based generation and manipulation of .grc files.
• Custom Client Integration
  Use the MCP interface to connect with custom tools or bots for advanced SDR use cases.
• Validation and Testing of Flowgraphs
  Programmatically validate and test flowgraphs using the server, ensuring correctness and reducing errors.
• Modular SDR Development
  Extend or customize the server for specialized SDR workflows or research, leveraging its modular design.

How to set it up

Windsurf

1. Ensure Python >= 3.13, GNURadio (tested with GNURadio Companion v3.10.12.0), and UV are installed.
2. Clone the repository:
   git clone https://github.com/yoelbassin/gr-mcp
3. Install GNURadio following official instructions.
4. Set up a UV environment:

   cd gr-mcp
   uv venv --system-site-packages
   

5. Add the MCP server to your Windsurf configuration:

   "mcpServers": {
     "gr-mcp": {
       "command": "uv",
       "args": [
         "--directory",
         "/path/to/gr-mcp",
         "run",
         "main.py"
       ]
     }
   }
   

6. Save the configuration and launch Windsurf.

Claude

1. Prerequisites: Python >= 3.13, GNURadio, UV installed.
2. Clone and set up as above.
3. Add this to your Claude Desktop configuration:

   "mcpServers": {
     "gr-mcp": {
       "command": "uv",
       "args": [
         "--directory",
         "/path/to/gr-mcp",
         "run",
         "main.py"
       ]
     }
   }
   

4. Save and restart Claude Desktop.
5. Verify connection in the MCP server list.

Cursor

1. Install Python >= 3.13, GNURadio, and UV.
2. Clone and initialize the repository as above.
3. Update your Cursor configuration:

   "mcpServers": {
     "gr-mcp": {
       "command": "uv",
       "args": [
         "--directory",
         "/path/to/gr-mcp",
         "run",
         "main.py"
       ]
     }
   }
   

4. Save and restart Cursor.
5. Confirm the MCP server is active.

Cline

1. Ensure all dependencies (Python, GNURadio, UV) are installed.
2. Clone and set up the GNURadio MCP server.
3. Edit Cline’s configuration as shown:

   "mcpServers": {
     "gr-mcp": {
       "command": "uv",
       "args": [
         "--directory",
         "/path/to/gr-mcp",
         "run",
         "main.py"
       ]
     }
   }
   

4. Save changes and restart Cline.
5. Check server connectivity.

Securing API Keys

If you need to pass API keys or secrets to the MCP server, use environment variables in your configuration. For example:

"mcpServers": {
  "gr-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/gr-mcp",
      "run",
      "main.py"
    ],
    "env": {
      "MY_SECRET_API_KEY": "your_api_key_here"
    },
    "inputs": {}
  }
}

Note: Replace "MY_SECRET_API_KEY" and value as needed for your environment.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "gr-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "gr-mcp" to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above tables, the GNURadio MCP Server offers solid core functionality and clear setup instructions, but lacks documentation for prompts, explicit resources, and tools, which limits its extensibility and usability out-of-the-box. Rating: 5/10.

MCP Score