I
io.github.ykshah1309/live-audio-intelligence-mcp
Live webcast transcription + vocal stress analysis (F0 jitter, hesitation) for earnings calls.
ActiveNot tested
27DNo CVEsactive
Quick Install
{
"mcpServers": {
"io-github-ykshah1309-live-audio-intelligence-mcp": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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[](https://mcppedia.org/s/io-github-ykshah1309-live-audio-intelligence-mcp)
Should you use this server?
Live webcast transcription + vocal stress analysis (F0 jitter, hesitation) for earnings calls.
✓
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
✓
Is it maintained?
Last commit 0 days ago.
i
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
README
live-audio-intelligence-mcp
Institutional-grade MCP server for live financial webcast transcription and vocal stress analysis.
Turns any live webcast URL (earnings calls, CNBC, investor days) into a real-time pipeline that feeds an LLM two things simultaneously:
- A rolling transcript via
faster-whisper(CPU, int8). - A vocal stress score (0–100) derived from F0 pitch jitter, hesitation ratio, and voiced-frame fraction — prosodic features correlated with executive nervousness, evasion, and guidance risk.
Built on the Model Context Protocol. Exposes 4 tools over stdio; drop it into Claude Desktop, Claude Code, or any MCP client.
Why this exists
Sell-side analysts and hedge-fund PMs don't just want to read the earnings transcript after the fact — they want a real-time signal about how confident the CFO sounds when asked about Q4 guidance. This server wires a Whisper pipeline and a pYIN-based prosody analyzer directly into an LLM's tool loop, so the model can ask "what did the CEO just say about China?" and "how stressed did they sound saying it?" in the same conversation.
Install
Requires Python ≥ 3.10 and ffmpeg on your PATH.
pip install live-audio-intelligence-mcp
Verify ffmpeg:
ffmpeg -version
The first run will download the faster-whisper base.en model (~140 MB).
Run it
Stdio MCP server:
live-audio-intelligence-mcp
Or equivalently:
python -m live_audio_intelligence_mcp
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"live-audio-intelligence": {
"command": "live-audio-intelligence-mcp"
}
}
}
Claude Code
claude mcp add live-audio-intelligence -- live-audio-intelligence-mcp
Tools
| Tool | Purpose |
|---|---|
monitor_live_stream(url, disable_vad=False) | Resolve the audio URL, spawn ffmpeg, start chunking + transcription. Returns a stream_id. |
get_rolling_transcript(stream_id, minutes_back=10) | Get the last N minutes of concatenated transcript text. |
analyze_speaker_stress(stream_id, time_window_seconds=60) | Run prosody analysis over the last N seconds of audio. Returns stress score, pitch jitter, hesitation ratio, pause stats, and a human-readable interpretation. |
stop_monitor(stream_id) | Kill ffmpeg, clean up temp files, drop the transcript buffer. |
The stress score
| Score | Interpretation |
|---|---|
| 0–20 | Confident, fluent delivery |
| 20–45 | Normal variation |
| 45–75 | Elevated stress — worth monitoring |
| 75–100 | High stress — potential market-moving signal |
Composite of:
- Pitch jitter (coefficient of variation of F0) — 50% weight
- Hesitation ratio (fraction of audio in pauses > 400 ms) — 35% weight
- Unvoiced fraction (speaker trailing off) — 15% weight
Low-SNR mode
For speakerphone audio (most earnings Q&A), pass disable_vad=true to
monitor_live_stream. Silero VAD tends to aggressively classify muddy
conference-call speech as silence; disabling it preserves more of the speech
at the cost of transcribing a bit more ambient noise.
Architecture
┌──────────────────┐
URL ─────▶ │ yt-dlp resolve │
└────────┬─────────┘
│ audio URL
▼
┌──────────────────┐ ┌────────────────┐
│ ffmpeg (bg) │ ───▶ │ 15s WAV chunk │
│ 16kHz mono PCM │ │ queue │
└──────────────────┘ └───────┬────────┘
│
┌──────────────────┴────────────────┐
▼ ▼
┌──────────────────┐ ┌──────────────────┐
│ faster-whisper │ │ librosa.p
... [View full README on GitHub](https://github.com/ykshah1309/live-audio-intelligence-mcp#readme)
Test This Server
No automated test available for this server. Check the GitHub README for setup instructions.
Help improve this page
This server is missing a description. Tools and install config are also missing.If you've used it, help the community.
Add informationScore Breakdown
Security
2/30No known vulnerabilities.
○
Known CVEs — No package registry to scan
○
Tool safety — No tools to analyze
○
Tool poisoning — No tools to analyze
○
Injection vectors — No tools to analyze
○
Tool stability — First scan — baseline recorded
○
Dependency health — No package to analyze
✗
License — No license specified
○
Authentication — No authentication required
○
Repository — active repo
CVEs checked daily via OSV.dev. Score algorithm is open source.
Reviews
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Frequently Asked Questions
- Is io.github.ykshah1309/live-audio-intelligence-mcp safe to use?
- io.github.ykshah1309/live-audio-intelligence-mcp has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
- How do I install io.github.ykshah1309/live-audio-intelligence-mcp?
- io.github.ykshah1309/live-audio-intelligence-mcp can be installed by cloning its GitHub repository and following the setup instructions in the README.
- What AI clients work with io.github.ykshah1309/live-audio-intelligence-mcp?
- io.github.ykshah1309/live-audio-intelligence-mcp is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.
- Is io.github.ykshah1309/live-audio-intelligence-mcp actively maintained?
- io.github.ykshah1309/live-audio-intelligence-mcp is actively maintained — last commit was 0 days ago.
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