Media
通过电平、信噪分离、混响尾音、高频存在感、削波和语音频段能量估计语音清晰度。
用三种语言从你的代码中调用此工具。
# 1) Request a presigned URL → returns { uploadUrl, storageKey }
curl -X POST 'https://api.elysiatools.com/api/upload/presign/audio-speech-intelligibility-score' \
-H 'Content-Type: application/json' \
-d '{"filename":"audioFile.ext","contentType":"application/octet-stream","size":12345}'
# 2) PUT the file bytes directly to the presigned uploadUrl
curl -X PUT '<presigned uploadUrl>' \
--data-binary @/path/to/file.ext
# 3) Call the tool, passing the returned storageKey for each file field
curl -X POST 'https://api.elysiatools.com/zh/api/tools/audio-speech-intelligibility-score' \
-F 'audioFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext'以 JSON 形式 POST 提交输入参数。文件类型参数需先单独上传。
POST https://api.elysiatools.com/zh/api/tools/audio-speech-intelligibility-score| 参数名 | 类型 | 必填 | 说明 |
|---|---|---|---|
| audioFile | file需先上传 | 是 | — |
JSON 结果
{
"key": {...},
"metadata": {
"key": "value"
},
"error": "Error message (optional)",
"message": "Notification message (optional)"
}将此工具加入你的 Model Context Protocol 服务，让 AI 智能体可以列出并调用它。
将以下内容加入你的 MCP 客户端配置：
{
"mcpServers": {
"elysiatools-audio-speech-intelligibility-score": {
"name": "audio-speech-intelligibility-score",
"description": "通过电平、信噪分离、混响尾音、高频存在感、削波和语音频段能量估计语音清晰度。",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=audio-speech-intelligibility-score",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}连接到 SSE 端点后，列出已开放的工具：
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}通过工具 id 调用，参数由其参数表构建：
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "audio-speech-intelligibility-score",
"arguments": {
"audioFile": "https://example.com/file.ext"
}
}
}有问题或反馈？请联系 [email protected]