Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TonePerfect MCP server Assess my French pronunciation in ./bonjour.wav against 'Bonjour'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Use the TonePerfect pronunciation assessment API from an MCP client. Supports recordings of a known reference text in ten languages. All language packs are experimental; scored dimensions vary by language. Japanese pitch accent is not scored. Phoneme evidence is not a validated correction.
Requires Node.js 18 or later. From this directory:
npm ci
npm testConfigure your MCP client with the absolute path to the built entry point:
{
"mcpServers": {
"toneperfect": {
"command": "node",
"args": ["/absolute/path/to/toneperfect-mcp/dist/index.js"],
"env": { "TONEPERFECT_API_KEY": "YOUR_API_KEY" }
}
}
}Get a key from the dashboard. Keep it in your client's secret configuration, never a public repository. For an npm installation, use npx -y toneperfect-mcp@0.1.0 as the command (or configure command: npx with args: ["-y", "toneperfect-mcp@0.1.0"]). The package publisher is niktoneperfect.
Related MCP server: tts-mcp
list_languages: fetch current supported languages, dimensions and limitations. No key or credits required.
assess_pronunciation: upload a local audio file, base64 audio, or audio downloaded from a URL, with reference text and locale. Sends that audio and reference text to TonePerfect. Only supply recordings you have permission to upload. Uses API credits.
explain_result: explain a previously returned JSON result locally. No API call or credits.
Recordings must be at most 10 MB and 30 seconds. One credit is charged per started 15 seconds of assessable audio; unassessable results are not charged. New accounts currently receive 50 free credits. See pricing for current terms.
The default assessment idempotency key is derived from the audio and fields: identical calls replay within the API's retention window. Set a new explicit key for an intentionally new assessment of identical input. Scores are display grades, not accuracy percentages or exam levels. Check assessable and the language-specific capabilities before displaying feedback.
npm run pack:mcpbThis builds and validates toneperfect.mcpb for compatible desktop clients. It contains the server and dependencies, not your key. This is a local stdio server, not a hosted HTTP connector.
server.json targets app.toneperfect/pronunciation. Publishing requires both an available npm package and verification of the domain namespace. The file alone does not create a registry listing. The MCP registry listing requires a separate publication step; installing the npm package does not create that listing.
MIT licensed. API reference. Contact: nik@toneperfect.app.
Score a recording of someone reading a known reference text (POST /v1/assess). Give the audio as a local file path, base64, or an http(s) URL (WAV, MP3, M4A, Ogg or WebM; 0.15-30 s; max 10 MB), plus the reference text and locale. Returns a short summary and the full JSON (sentence, word and sound scores, expected vs heard, timings, billing). Uses the API key in TONEPERFECT_API_KEY; 1 credit per started 15 s of assessable audio.
Inputs: task, text, pinyin, filename, +5 more.
Explain a TonePerfect assessment JSON (the output of assess_pronunciation) word by word: what each score means, which sounds were flagged and what to practise. Runs locally; no API call, no credits.
Inputs: result (required).
List the languages, scored dimensions and limits of the TonePerfect pronunciation assessment API (GET /v1/capabilities). Free, no API key needed.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 3 tools
Each tool maps to a clearly distinct stage of the workflow: discovery (list_languages), assessment (assess_pronunciation), and post-hoc interpretation (explain_result). There is no overlap in inputs or outputs, and explain_result explicitly references the output of assess_pronunciation, reinforcing the pipeline.
All three names follow a strict snake_case verb_noun pattern (list_languages, assess_pronunciation, explain_result). The verbs are accurate and consistent in style, with no mixed conventions.
Three tools is a tight, well-justified surface for a focused API wrapper covering capabilities, scoring, and result explanation. Every tool earns its place and none appears redundant or thin.
The core lifecycle (discover capabilities, assess audio, explain scores) is fully covered with no dead ends for the primary use case. Minor gaps remain, such as checking credit balance or listing/batch-assessing multiple recordings, but these are workarounds rather than blockers.
Pronunciation assessment, phoneme scoring, speaker voice ID, audio transcription, speech synthesis.
Pronunciation scoring, speech-to-text, and text-to-speech for language learning
MCP server for Speech-to-Text
FalaAI API MCP server: transcribe audio, diagnose conversations, audit compliance (ISO 18295-1).
