MCP 服务器

CHIVOX speech MCP

Gives your AI agent real-time pronunciation scoring and multi-dimensional speech assessment through CHIVOX server.

README

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TL;DR — LLMs can't hear audio. Chivox MCP is a hosted MCP server that scores pronunciation at the phoneme level — Mandarin tones included. One tools/call returns overall / accuracy / pron / fluency / details[].phone[] (pronunciation, fluency, per-phoneme breakdown) in a stable JSON shape your model can reason over. Not STT. Not a Whisper wrapper.

On this page: Fit check · Quickstart · Response JSON · Tools · Transport · Compare · Coach loop · Mandarin · English · Pricing · FAQ

🎯 Is this for you?

Most production teams run Whisper + Chivox together: Whisper to transcribe what was said, Chivox to score how well. They don't compete.

🚀 Quickstart

Hosted endpoint: https://mcp-global.cloud.chivox.com · every request needs Authorization: Bearer <api_key>. Get a key →

Client	Setup
Cursor	`~/.cursor/mcp.json` — IDE MCP, zero install
LangChain	LangGraph ReAct agent + MCP adapter
OpenAI Agents SDK	`agents.mcp.MCPServerStreamableHttp`
Claude Desktop	Local proxy for mic streaming
Raw MCP SDK	Direct `mcp` Python client

Cursor (zero install)

// ~/.cursor/mcp.json
{
  "mcpServers": {
    "chivox-speech-eval": {
      "type": "streamable-http",
      "url": "https://mcp-global.cloud.chivox.com",
      "headers": { "Authorization": "Bearer <your_api_key>" }
    }
  }
}

LangChain

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

client = MultiServerMCPClient({
    "chivox": {
        "transport": "streamable_http",
        "url": "https://mcp-global.cloud.chivox.com",
        "headers": {"Authorization": "Bearer <your_api_key>"},
    }
})
tools = await client.get_tools()  # discovers all 16 tools

agent = create_react_agent("openai:gpt-4o-mini", tools)
result = await agent.ainvoke({"messages": [(
    "user",
    "Score https://example.com/audio/sentence.mp3, ref: I think therefore I am",
)]})

OpenAI Agents SDK

from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

chivox = MCPServerStreamableHttp(
    params={
        "url": "https://mcp-global.cloud.chivox.com",
        "headers": {"Authorization": "Bearer <your_api_key>"},
    },
    name="chivox-speech-eval",
)

async with chivox:
    agent = Agent(
        name="coach",
        instructions="Professional speaking coach",
        mcp_servers=[chivox],
    )
    r = await Runner.run(
        agent,
        "Score https://example.com/audio/sentence.mp3, ref: I think therefore I am",
    )
    print(r.final_output)

Claude Desktop (mic streaming via local proxy)

npm install -g chivox-local-mcp

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "chivox": {
      "command": "chivox-local-mcp",
      "env": {
        "MCP_REMOTE_URL": "https://mcp-global.cloud.chivox.com",
        "MCP_API_KEY": "<your_api_key>"
      }
    }
  }
}

Raw MCP SDK

import asyncio
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

async def main():
    async with streamablehttp_client(
        "https://mcp-global.cloud.chivox.com",
        headers={"Authorization": "Bearer <your_api_key>"},
    ) as (r, w, _):
        async with ClientSession(r, w) as s:
            await s.initialize()
            out = await s.call_tool("en_sentence_eval", {
                "ref_text": "I think therefore I am",
                "audio_url": "https://example.com/audio/sentence.mp3",
            })
            print(out)

asyncio.run(main())

More clients (Claude Code, Windsurf, Zed, Mastra, function-calling mode) → docs → Clients

🧠 What the LLM actually sees

Every tool returns the same top-level shape — switch locale or granularity with zero schema work. Example for "hello":

{
  "overall": 85,
  "accuracy": 82,
  "pron": 88,
  "integrity": 95,
  "fluency": { "overall": 78, "speed": 65, "pause": 2 },
  "details": [
    {
      "char": "hello",
      "score": 85,
      "phone": [
        { "phoneme": "h",  "score": 90, "dp_type": "normal" },
        { "phoneme": "ɛ",  "score": 82, "dp_type": "normal" },
        { "phoneme": "l",  "score": 88, "dp_type": "normal" },
        { "phoneme": "oʊ", "score": 80, "dp_type": "normal" }
      ]
    }
  ]
}

For English mispronunciations, phoneme_error: { expected, actual } is included. Mandarin adds tone_ref / tone_detected with sandhi-aware dp_type verdicts. Full field list →

🛠️ Tools catalog

Inline audio: pass audio_url or audio_base64 in the tool call — no upload round-trip. Formats: mp3 · wav · ogg · m4a · aac · pcm. Per-tool notes →

🔌 Dual transport

Two ways to feed audio — same result shape, different UX. Function-calling fallback: fc-global.cloud.chivox.com.

⚖️ How it compares

Rule of thumb — use Whisper to know what was said; use Chivox to know how well. They stack.

💬 …and here's what your LLM does with it

Pipe that JSON straight into any chat model with a one-line system prompt — "You are a warm pronunciation coach. Diagnose, then drill." — and you get a real lesson back. No fine-tuning. No audio understanding. Just chat.completion.

Why this works — the LLM never "heard" the audio. The JSON names the problem in fields it already understands (dp_type: "mispron", phoneme_error.actual, tone_ref vs tone_detected), so a vanilla chat.completion can diagnose like a human teacher.

🔁 The three-stage loop

🎤 Input: 1-minute learner recording → Output: warm feedback + targeted drill, end-to-end in < 1.6 seconds.

<div align="center">Compatible with GPT · Claude · Gemini · DeepSeek · Llama · Mistral · Qwen · GLM — any model with tool / function-calling support.</div>

🏮 The moat: a tireless Mandarin tutor

30M+ learners worldwide study Mandarin — including heritage speakers and adult beginners — yet few platforms score tone errors (mā / má / mǎ / mà) at the phoneme level in English. Chivox's Chinese engine is trained on the same data that powers China's Putonghua Proficiency Test (普通话水平测试, PSC).

🇬🇧 And yes — exam-grade English too

Exam-grade rubrics on the same MCP endpoints: IELTS · TOEFL · Cambridge YLE · K-12 reading assessments for English, plus PSC-aligned Mandarin scoring. Same JSON shape, 20+ scoring dimensions — just change ref_text and accent.

💎 Why developers ship with Chivox MCP

Plus: streaming + inline modes · TLS 1.3 end-to-end · audio discarded after scoring (JSON retained 30 days) · on-prem available for enterprise · limits & privacy →

💳 Pricing

Honest defaults. Start with 600 free calls (30 days) and all 16 tools unlocked — no feature gates, no card. When you need more, pay per successful call at tiered rates — the more you ship, the cheaper each call gets.

Free tier ≠ crippled tier. Every new account gets 600 free calls valid for 30 days with the full 16-tool catalog — same engine, same JSON, same SLA as paid keys. After the trial window or when calls are used up, top up from $10 and let the volume tiers do the rest. Failed calls are never billed.

❓ FAQ

Is this just another wrapper around Whisper?

No. Whisper transcribes; Chivox scores. The engine is trained on exam-graded samples and returns phoneme-level details[].phone[] — not a transcript. Most teams run both.

Does it work offline / on-device?

The hosted MCP server needs outbound access to the scoring engine. For air-gapped deployments, contact us — we ship an on-prem container for enterprise customers.

What about dialects and accents?

Mandarin targets standard Pǔtōnghuà with sandhi-aware tone verdicts. English supports en-US, en-GB, and en-AU rubrics via locale parameters on the relevant tools.

Which LLMs work out of the box?

Any model with OpenAI-style function calling: GPT-4o / 5.x, Claude Sonnet / Opus, Gemini, DeepSeek, GLM, Kimi, Doubao, Qwen. Tool schemas are forwarded verbatim.

Can I use this in a browser?

For quick demos, yes — but production traffic should flow through your backend so the API key stays server-side. Privacy notes →

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