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MCP Server Pronunciation

by JuhongPark

Local voice practice for English conversation, pronunciation drills, phoneme feedback, grammar, fluency, and microphone checks.

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Install MCP Server Pronunciation

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "juhongpark-mcp-server-pronunciation": {
      "command": "uvx",
      "args": [
        "mcp-server-pronunciation"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Automated check passed— started and listed 8 tools correctly (1mo ago).

Overview

JuhongPark/mcp-server-pronunciation MCP server records speech locally, transcribes it with faster-whisper, and returns English pronunciation, grammar, fluency, or phoneme-level feedback. Its MCP tools support free-form voice conversations, sentence drills, retries, suggested practice prompts, raw WAV recording, and microphone diagnostics. Use it for local English coaching when you want speech analysis inside an MCP client rather than a text-only tutor. An optional phoneme extra adds wav2vec2 forced alignment and more detailed reference-sentence assessment.

Use cases

  • Practice English pronunciation with a reference sentence
  • Hold voice conversations with inline grammar and fluency feedback
  • Retry a pronunciation drill after receiving assessment
  • Check microphone devices before recording
  • Generate focused drills for common phoneme areas

Key features

  • Local microphone recording with silence detection
  • faster-whisper transcription on device
  • Phoneme-level reference-sentence assessment
  • English grammar and fluency feedback
  • Practice sentence suggestions by focus and difficulty
  • Optional wav2vec2 forced alignment

Tools8

What MCP Server Pronunciation lets your agent do. Expand a tool for its parameters.

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Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

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Scanned 8d ago via OSV.dev · mcp-server-pronunciation (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars3Stargazers on the source repository.
Last commit17d agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed8Callable tools this server registers over MCP.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Server Pronunciation works

What JuhongPark/mcp-server-pronunciation MCP server does

JuhongPark/mcp-server-pronunciation MCP server turns microphone input into English-learning feedback for an MCP assistant. The converse tool is intended for open-ended voice chat: it records until silence or the duration limit, transcribes the speech, and returns brief pronunciation, grammar, and fluency notes along with guidance for the assistant. The practice tool is intended for reading a supplied sentence and produces a more detailed pronunciation assessment.

For targeted practice, suggest_sentence selects a sentence by phoneme focus and difficulty, while quick_practice selects one and immediately records the learner. retry repeats the most recent practice target. The available focus values are th, f_v, r_l, vowels, and general; difficulty values are beginner, intermediate, and advanced.

How it works

Audio is captured from the local microphone and processed on the user’s machine. Recordings stop automatically after detected silence, although each recording tool also accepts a maximum duration.

When a reference sentence is supplied, the assessment aligns the spoken words with the expected text and can report expected versus produced IPA, weak phonemes, prosody observations, and learner-profile hints. The bundled profile includes Korean-L1 patterns such as r/l confusion, th-to-s substitutions, final cluster deletion, and an intrusive onset vowel. With the optional [phoneme] installation, wav2vec2 CTC forced alignment provides an additional acoustic check intended to reduce transcription-related errors for unusual names and domain terms. Without a reference sentence, assess reports the transcript and prosody information rather than a full word-level pronunciation comparison.

Setup and configuration

The project requires Python 3.11 or newer and a working microphone. The documented launcher is uvx mcp-server-pronunciation; it can also be installed with pip or as a uv tool. Linux users may need to install PortAudio through their system package manager. The default Whisper model uses about 150 MB of disk space, and the optional phoneme extra requires additional model storage.

Run uvx mcp-server-pronunciation doctor to check the environment before connecting an MCP client. pull-model base.en can pre-download the default model. MCP client examples are provided for Claude Desktop, Cursor, Codex CLI, Claude Code, and VS Code. The JuhongPark/mcp-server-pronunciation MCP server runs over stdio in these configurations.

Tools and capabilities

  • converse: record, transcribe, and provide quick conversational feedback.
  • practice: assess reading of a specified sentence.
  • quick_practice: choose a sentence and start a drill.
  • retry: record the previous practice sentence again.
  • suggest_sentence: return a sentence with its focus and difficulty.
  • record: save an unassessed microphone recording as a WAV file.
  • assess: analyze the latest recording or a specified WAV file.
  • check_mic: list available input devices and identify the default microphone.

Assessment results include rendered reports and structured values such as transcript, clarity percentage, speaking rate, top issue, next action, and retry comparison.

Limitations and notes

Feedback is a coaching signal, not a standardized-test, clinical, employment, or other high-stakes assessment. The README warns that transcripts, pronunciation results, recording behavior, and runtime stability may be inaccurate or platform-dependent. A microphone is required for recording features, and the optional forced-alignment functionality adds dependencies and model storage. The JuhongPark/mcp-server-pronunciation MCP server currently documents a Korean-L1 learner profile; additional learner profiles are not described as built in.

Read the full README View source on GitHub

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Frequently Asked Questions about MCP Server Pronunciation

Run `uvx mcp-server-pronunciation`.

Yes. The README provides a Claude Desktop stdio configuration using `uvx` and `mcp-server-pronunciation`.

Call the `practice` tool with the sentence as `reference_text`. Use `retry` to record the same target again.

Use `check_mic` to list available audio input devices and identify the default device. The `doctor` command is also documented for preflight checks.

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