Radio France Podcast Explorer MCP
LocalAbout Radio France Podcast Explorer MCP
Radio France Podcast Explorer MCP is an MCP server for AI assistants seeking Radio France podcasts and audio content. It supports topic searches, station and taxonomy browsing, and audio metadata retrieval.
Key features
- 01Search podcasts and episodes by topic
- 02Browse station programs
- 03Explore themes, categories, and tags
- 04Retrieve episode details and audio URLs
- 05Find content from natural-language queries
Use cases
- 01Find podcasts about a subject
- 02Check programming on a Radio France station
- 03Browse available podcast categories
MCP support
Exposes Radio France podcast and audio search and retrieval capabilities to MCP clients, including episode details, metadata, and stream URLs.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMERadio France Podcast Explorer MCP
A Model Control Protocol (MCP) server that allows AI assistants like Claude to search for and explore podcasts, audio replays, and other content from Radio France.
Features
- Search for podcasts and episodes by topic
- Get program information for specific Radio France stations
- Explore content organized by taxonomies (themes, categories, tags)
- Retrieve detailed podcast and episode information
- Process natural language queries to find relevant content
- Get audio content metadata and stream URLs
Requirements
- Python 3.7+
- Radio France API Key (obtain from Radio France Open API)
Installation
-
Clone this repository:
git clone https://github.com/infinitimeless/radiofrance-podcast-explorer-mcp.git cd radiofrance-podcast-explorer-mcp -
Create a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate -
Install dependencies:
pip install -r requirements.txt -
Create a
.envfile with your Radio France API key:RADIOFRANCE_API_KEY=your_api_key_here
Testing
Before running the server, you can verify your installation and API key by running the tests:
python run_tests.py
You can also run the GraphQL schema test to verify API structure:
python tests/test_schema.py
Running the Server
python server.py
Using with Claude Desktop
To use this server with Claude Desktop:
-
Open the file at the following location:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
- macOS:
-
Add or update the server configuration:
{ "mcpServers": { "radiofrance": { "command": "python", "args": ["/absolute/path/to/radiofrance-podcast-explorer-mcp/server.py"], "env": { "RADIOFRANCE_API_KEY": "your_api_key_here" } } } } -
Save the file and restart Claude Desktop
Development Testing
You can test the server using the MCP Inspector:
npx @modelcontextprotocol/inspector python server.py
This will open a web interface where you can test each of the server's tools individually.
API Structure
The Radio France API uses a taxonomy-based approach:
- Taxonomies: Categories, tags, and themes that organize content
- Diffusions: Content items (episodes, programs) associated with taxonomies
- Brands: Shows and podcast series
- Grid: Program schedules for stations
Documentation
For more detailed information, see:
- Usage Guide - Detailed instructions on using the server
- API Information - Information about the Radio France API
Example Queries for Claude
Here are some example queries you can ask Claude when using this MCP server:
- "Find me podcasts about French history"
- "What's currently playing on France Inter?"
- "Search for podcast episodes about European politics"
- "What are the different podcast categories on Radio France?"
- "Get me information about the audio content at this URL"
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT
Comments
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.