FunASR MCP Server

Model Context Protocol server that gives AI assistants the ability to transcribe audio.

Setup

1. Install dependencies

pip install funasr

2. Optional: run with Docker

The Dockerfile starts the MCP server over stdio and is suitable for MCP directory checks that initialize the server and call tools/list.

docker build -t funasr-mcp examples/mcp_server
docker run --rm -i \
  -e FUNASR_DEVICE=cpu \
  -v /path/to/audio:/audio:ro \
  funasr-mcp

When submitting this server to MCP directories such as Glama, use this folder as the Docker build context so the container entrypoint runs funasr_mcp.py. The glama.json file in this directory declares the MCP server command and metadata for directory scanners.

Glama submission checklist

Use these values when adding the server at https://glama.ai/mcp/servers:

Field	Value
Repository URL	https://github.com/modelscope/FunASR
Docker build context	`examples/mcp_server`
Dockerfile path	`examples/mcp_server/Dockerfile`
Server command	`python /app/funasr_mcp.py`
Expected MCP tool	`transcribe_audio`

After Glama finishes evaluation, verify that the score badge endpoint returns success before adding it to directory PRs:

[![modelscope/FunASR MCP server](https://glama.ai/mcp/servers/modelscope/FunASR/badges/score.svg)](https://glama.ai/mcp/servers/modelscope/FunASR)

If the badge endpoint still returns 404, keep the badge out of external directory submissions until the Glama listing is live.

3. Configure your AI tool

Claude Code (~/.claude.json):

{
    "mcpServers": {
        "funasr": {
            "command": "python",
            "args": ["/path/to/examples/mcp_server/funasr_mcp.py"],
            "env": {"FUNASR_DEVICE": "cuda"}
        }
    }
}

Claude Desktop (claude_desktop_config.json):

{
    "mcpServers": {
        "funasr": {
            "command": "python",
            "args": ["/path/to/funasr_mcp.py"],
            "env": {"FUNASR_DEVICE": "cpu"}
        }
    }
}

Cursor (Settings → MCP Servers → Add):

Command: python /path/to/funasr_mcp.py
Environment: FUNASR_DEVICE=cuda

Tools

`transcribe_audio`

Transcribe a speech audio file to text.

Parameters:

Name	Type	Required	Description
`audio_path`	string	Yes	Path to audio file (wav, mp3, flac, m4a, ogg)
`language`	string	No	Language hint (auto-detected by default)

Returns: Transcribed text with timestamps and speaker labels (when available).

Example Usage

Once configured, ask your AI assistant:

"Transcribe the meeting recording at ~/Downloads/meeting.wav"
"What was said in this audio file? /path/to/interview.mp3"
"Convert this voice memo to text: ~/voice_note.m4a"

Environment Variables

Variable	Default	Description
`FUNASR_DEVICE`	`cpu`	Device: `cuda`, `cpu`, or `mps`
`FUNASR_MODEL`	`iic/SenseVoiceSmall`	ASR model to use

Features

50+ languages with automatic detection
Speaker diarization — identifies who said what
Timestamps — per-segment timing
170x realtime on GPU, 17x on CPU
No API key needed — fully local inference
MIT licensed, privacy-friendly (audio never leaves your machine)

Verified Compatibility

Tool	Status
Claude Code	✅ Tested
Claude Desktop	✅ Compatible
Cursor	✅ Compatible
Windsurf	✅ Compatible
Any MCP client	✅ Standard protocol