GR-MCP: GNURadio MCP Server

GR-MCP: Automating GNURadio with the Model Context Protocol

The GR‑MCP server fills a critical gap for engineers and researchers working with software-defined radio (SDR). Traditional SDR development relies on the GNURadio Companion’s graphical interface, which is powerful but manual and difficult to automate. GR‑MCP exposes GNURadio’s flowgraph capabilities through the Model Context Protocol, allowing large language models (LLMs), bots, and custom automation tools to create, modify, and validate files programmatically. By treating flowgraph construction as a first‑class API call, developers can reduce the time spent on repetitive design tasks and focus on higher‑level algorithm development.

At its core, GR‑MCP offers a robust MCP interface that mirrors the structure of GNURadio blocks and parameters. Clients can request available resources, invoke tools to add or remove blocks, and query the current state of a flowgraph. The server also supports direct generation of files, enabling seamless integration with CI/CD pipelines or cloud‑based SDR deployments. Because the server is written in Python 3.13+ and relies on GNURadio’s native libraries, it can run locally or in containerized environments without sacrificing performance.

Key capabilities include:

• Programmatic Flowgraph Creation: Build complex signal chains by specifying block types and connection details in a declarative manner.
• LLM & Automation Readiness: Designed from the outset for interaction with LLMs, allowing natural language instructions to be translated into flowgraph modifications.
• Extensibility: A modular architecture lets users add custom blocks or tooling without touching the core server code.
• Validation and Testing: Built‑in unit tests ensure that flowgraphs generated by external clients remain syntactically correct and executable.
• Rich Examples: The repository ships with ready‑to‑run examples that serve as templates for common SDR scenarios.

Real‑world use cases span rapid prototyping of communication protocols, automated generation of test vectors for hardware verification, and dynamic reconfiguration of SDR networks in response to changing channel conditions. For instance, an AI assistant can be instructed to “create a QPSK transmitter with a 1 MHz bandwidth” and the server will return a fully configured flowgraph ready for deployment. In research labs, GR‑MCP can power automated sweeps across modulation schemes, collecting performance metrics without manual intervention.

Integrating GR‑MCP into existing AI workflows is straightforward: clients register the server in their configuration, then issue MCP commands as part of a larger pipeline. Because the protocol is language‑agnostic, any LLM platform that supports MCP—Claude Desktop, Cursor, or custom agents—can leverage GR‑MCP to control SDR hardware directly from natural language prompts. This tight coupling between AI reasoning and low‑level radio engineering unlocks new possibilities for rapid experimentation, educational tools, and scalable SDR services.