About Vocametrix
Vocametrix is an MCP server for speech-language clinicians and other users of voice and speech analysis. It provides acoustic metrics, pronunciation and transcription tools, and therapy-planning workflows through the Vocametrix API.
Key features
- 01Acoustic voice-quality metrics
- 02Pronunciation and pitch assessment
- 03Speech transcription and synthesis
- 04Clinical interpretation and exercise generation
- 05Therapy planning workflows
Use cases
- 01Assess voice recordings with acoustic measures
- 02Review pronunciation and speech transcription
- 03Generate exercises for speech therapy
- 04Create and review therapy plans
MCP support
Exposes Vocametrix API capabilities as MCP tools, resources, and prompts, including voice analysis, speech assessment, and therapy workflows. It supports hosted HTTP and local stdio connections; some tools are limited to local mode.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y @vocametrix/mcp-serverVOCAMETRIX_API_KEYSecretYour Vocametrix API key. Get one free at https://www.vocametrix.com/registrationLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
39Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| version | enum | No | AVQI algorithm versionv02.03 | v03.01 |
| sustainedVowelPath | string | Yes | Sustained vowel WAV file (e.g. /a/ held 3+ s) |
| connectedSpeechPath | string | No | Connected speech WAV file (optional, improves AVQI accuracy) |
Details
From the project README@vocametrix/mcp-server
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.
Claude Code
Hosted server (no local install):
claude mcp add --transport http vocametrix https://mcp.vocametrix.com/mcp --header "x-api-key: your-api-key-here"
Or run it locally with npx:
claude mcp add vocametrix -e VOCAMETRIX_API_KEY=your-api-key-here -- npx -y @vocametrix/mcp-server
Add --scope user right after claude mcp add to make Vocametrix available in all your projects. Then type /mcp in Claude Code to check the connection.
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)
| Tool | Description |
|---|---|
vocametrix_calculate_avqi | Acoustic Voice Quality Index (AVQI) — overall dysphonia severity |
vocametrix_calculate_dsi | Dysphonia Severity Index (DSI) |
vocametrix_calculate_cpp | Cepstral Peak Prominence — breathiness, hoarseness |
vocametrix_calculate_hnr | Harmonics-to-Noise Ratio (multi-band) |
vocametrix_calculate_jitter_shimmer | Period and amplitude perturbation |
vocametrix_calculate_voice_range_profile | Voice Range Profile |
vocametrix_calculate_prosody_similarity | Prosody similarity between two utterances |
Advanced voice analysis
| Tool | Description |
|---|---|
vocametrix_calculate_spectral | Spectral tilt, slope, and formant energy |
vocametrix_calculate_formants | Formant frequencies F1–F4 |
vocametrix_calculate_sz_ratio | S/Z phonation ratio |
vocametrix_calculate_gne | Glottal-to-Noise Excitation |
vocametrix_calculate_h1_h2 | H1–H2 harmonic difference |
vocametrix_calculate_abi | Acoustic Breathiness Index |
vocametrix_calculate_voice_dynamics | Dynamic range and fundamental frequency statistics |
vocametrix_measure_ddk | Diadochokinesis (pa-ta-ka): syllable rate, regularity, acceleration, Pierce 2013 norms for 65-86 |
Ingestion utilities
| Tool | Description |
|---|---|
vocametrix_upload_audio | Upload a WAV file (base64) → returns a stable blobUrl |
vocametrix_ingest_url | Ingest a public HTTPS WAV URL → returns a stable blobUrl |
Speech and pronunciation
| Tool | Description |
|---|---|
vocametrix_assess_pronunciation | Phoneme-level pronunciation scoring |
vocametrix_assess_pronunciation_with_pitch | Pronunciation + pitch analysis combined |
vocametrix_transcribe_audio | Streaming ASR transcription with progress |
vocametrix_synthesize_speech | Text-to-speech synthesis |
Audio measures
| Tool | Description |
|---|---|
vocametrix_measure_sound_level | Sound level in uncalibrated dB over a time window |
vocametrix_extract_egemaps | Geneva Minimalistic Acoustic Parameter Set, GeMAPS v01b (62 features) |
vocametrix_detect_phonemes | French phoneme detection with confidence scores and timestamps |
vocametrix_classify_stuttering | Dysfluency classification |
AI agents
| Tool | Description |
|---|---|
vocametrix_interpret_voice_metrics | Clinical interpretation of voice metrics |
vocametrix_generate_exercises | Personalized voice/speech exercise generation |
vocametrix_generate_word_list | Target word list generation for therapy |
vocametrix_chat_speech_therapist | Conversational AI speech-language therapist |
vocametrix_convert_french_to_ipa | French text → IPA phonetic transcription |
vocametrix_interpret_spelling_attempt | Spelling correction agent |
vocametrix_check_syntax | Syntax checking agent |
vocametrix_vocabulary_tutor | Vocabulary tutoring agent |
vocametrix_adapt_exercise | Adaptive exercise generation |
Therapy planning
Not published on the ChatGPT endpoint (/chatgpt/mcp): these tools take a patient identifier and clinical history, which the ChatGPT app guidelines treat as protected health information. They remain available over stdio and /mcp.
| Tool | Description |
|---|---|
vocametrix_generate_therapy_plan | Generate an AI therapy plan |
vocametrix_get_therapy_status | Poll therapy plan generation status |
vocametrix_get_therapy_result | Fetch completed therapy plan |
vocametrix_approve_therapy_plan | Approve a therapy plan |
Workflow tools
| Tool | Description |
|---|---|
vocametrix_full_voice_assessment | Parallel AVQI + CPP + HNR + jitter/shimmer + spectral |
vocametrix_batch_pronunciation | Assess a folder of WAV files. Reads the server's own filesystem, so it is only registered in stdio/local mode (VOCAMETRIX_MCP_LOCAL_FS=1) — not available on the hosted server |
vocametrix_full_therapy_workflow | Generate → poll → fetch → approval flow |
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 foravqi,dsi,cpp,hnr,jitter-shimmer,gne
Prompts
interpret_voice_assessment— Generate a clinical SLP-style interpretation report from assessment JSONcompare_pre_post_therapy— Quantified pre/post therapy narrative with metric-by-metric comparisongenerate_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:
| Input | Hosted / remote server | Stdio / local server (npx, Claude Desktop) |
|---|---|---|
https://... blobUrl from vocametrix_upload_audio | ✅ recommended | ✅ |
Public https://... URL to a WAV file | ✅ | ✅ |
Public URL via vocametrix_ingest_url → returned blobUrl | ✅ recommended for URL inputs | ✅ |
data:audio/wav;base64,... data URL | ✅ | ✅ |
| Raw base64 string (≥ 512 chars) | ✅ | ✅ |
Absolute local path (/home/..., C:\...) | ❌ rejected | ⚠️ requires VOCAMETRIX_MCP_LOCAL_FS=1 |
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
| Variable | Required | Description |
|---|---|---|
VOCAMETRIX_API_KEY | Yes | Your Vocametrix API key |
VOCAMETRIX_MCP_LOCAL_FS | No | Set to 1 to allow analysis tools to read absolute local file paths (stdio/local deployments only). Default off — local paths are rejected with an actionable error so chat clients are pushed toward the vocametrix_upload_audio → blobUrl workflow. |
VOCAMETRIX_MCP_ALLOW_PRIVATE_HOSTS | No | Set to 1 to allow fetching audio URLs whose host resolves to a private, loopback or link-local address (a LAN file server, for instance). Default off, including in stdio/local mode: the caller of an MCP tool is an LLM, and an LLM that has read a hostile page can be talked into pointing these tools at your own network. |
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
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
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.