@vocametrix/mcp-server

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Official Model Context Protocol server for the Vocametrix voice analysis API.

Gives any MCP-compatible AI assistant (Claude Desktop, Cursor, Cline, etc.) direct access to clinical voice metrics, pronunciation assessment, speech transcription, and AI-powered therapy planning.

Quick start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "vocametrix": {
      "command": "npx",
      "args": ["-y", "@vocametrix/mcp-server"],
      "env": {
        "VOCAMETRIX_API_KEY": "your-api-key-here"
      }
    }
  }
}

Get an API key at vocametrix.com/registration. MCP analysis uses purchased API credits; the website subscription and trial do not cover API calls.

ChatGPT (OAuth, API credits)

The optional /chatgpt/mcp endpoint links a user's API account through OAuth. The user approves access on the platform's consent page; ChatGPT receives revocable tokens, not the API key. Existing /mcp API-key clients and local stdio clients remain supported.

See ChatGPT setup and release checks for the matching platform changes, database migration, configuration, and tests. This integration must be deployed and tested in ChatGPT before public submission.

Tools

Voice quality (acoustic)

Advanced voice analysis

Ingestion utilities

Speech and pronunciation

Audio measures

AI agents

Therapy planning

Workflow tools

Resources

• vocametrix://docs/api — API quick reference (auth, rate limits, audio requirements, error codes)
• vocametrix://recording-guide — Recording protocols for every tool (sustained vowel, connected speech with language-specific reference sentences, glissando, sustained /s/ and /z/)
• vocametrix://thresholds/{metric} — Clinical reference thresholds for avqi, dsi, cpp, hnr, jitter-shimmer, gne

Prompts

• interpret_voice_assessment — Generate a clinical SLP-style interpretation report from assessment JSON
• compare_pre_post_therapy — Quantified pre/post therapy narrative with metric-by-metric comparison
• generate_session_report — SOAP-format progress note from pronunciation assessment data

Audio requirements

• Format: WAV (16-bit PCM recommended)
• Sustained vowel tasks: 3+ seconds of /a/ phonation
• Connected speech tasks: 5–30 seconds of read passage
• Minimum sampling rate: 16 kHz

How to pass audio to a tool

The audioPath parameter accepts several input types, but which ones are valid depends on how the MCP server is running:

For chat clients that attach audio in the conversation (Claude.ai web/mobile, etc.), the LLM cannot pass an absolute path to a hosted server — it must call vocametrix_upload_audio first with the file content base64-encoded, then pass the returned blobUrl as audioPath to any analysis tool. The MCP descriptions guide the LLM toward this workflow automatically.

For stdio/local deployments where the MCP runs on the user's own machine, set VOCAMETRIX_MCP_LOCAL_FS=1 to allow analysis tools to read absolute local paths directly — convenient for batch processing of files already on disk.

Environment variables

Development

git clone https://github.com/Vocametrix/vocametrix-mcp.git
cd vocametrix-mcp
npm install
npm run build
npm test            # run unit tests
npm run inspector   # test with MCP Inspector

MCP Registry

Listed in the official MCP Registry under io.github.pmarmaroli/vocametrix-mcp. Available for one-click installation in MCP-compatible clients (Claude Desktop, Cursor, Zed, Windsurf, and more).

Related projects

The Vocametrix ecosystem:

• 📘 Vocametrix API documentation — full reference for the underlying REST API powering this MCP server.
• 📐 OpenAPI 3.1 specification — machine-readable schema for all 49 endpoints.
• 🐍 vocametrix-python — official Python SDK if you want direct API access from Python (pip install vocametrix).
• 🟦 vocametrix-js — official TypeScript / JavaScript SDK used internally by this MCP server (npm install vocametrix).

License

MIT — see LICENSE