TonePerfect MCP server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| TONEPERFECT_API_KEY | Yes | Your TonePerfect API key from the dashboard. Used for pronunciation assessment requests. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_languagesA | List the languages, scored dimensions and limits of the TonePerfect pronunciation assessment API (GET /v1/capabilities). Free, no API key needed. |
| assess_pronunciationA | 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. |
| explain_resultA | 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
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.