Goose FM Radio
This MCP implementation, developed by Kim, provides a radio tuning interface for AI assistants. Built with Python and leveraging the FastMCP framework, it offers tools for tuning to specific FM frequencies and controlling audio playback. The implementation focuses on integrating with RTL-SDR hardware to enable real-world radio reception. By connecting AI models with radio functionality, this server allows for scenarios like voice-controlled radio tuning, automated station scanning, and audio content analysis. It's particularly useful for projects involving smart home integration, audio processing experiments, or enhancing AI assistants with live radio capabilities.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- iot-hardware
- Source
- PulseMCP
- Repository
- github.com/mccartykim/goose_fm
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve Goose FM Radio from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.