Audio Transcriber

CLI or API | MCP | Agent

Version: 2.1.0

Documentation — Installation, deployment, and usage across the CLI, Python API, MCP server, and A2A agent are maintained in the official documentation.

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Overview

Audio Transcriber is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Transcribe your .wav .mp4 .mp3 .flac files to text or record your own audio!.

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Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

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CLI or API

This agent wraps the Transcribe your .wav .mp4 .mp3 .flac files to text or record your own audio! API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

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MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

The table below is auto-generated from the live server — do not edit by hand.

<!-- MCP-TOOLS-TABLE:START -->

Condensed action-routed tools (MCP_TOOL_MODE=condensed)

| MCP Tool | Toggle Env Var | Description | |----------|----------------|-------------| | audio_ingest_transcription | MISCTOOL | Transcribe an audio file and natively ingest it into the knowledge graph. | | health_check | MISCTOOL | | | transcribe_audio | AUDIO_PROCESSINGTOOL | Transcribes audio from a provided file or by recording from the microphone. | | transcribe_media | MEDIA_SIDECARTOOL | Verify the digest, transcribe via Whisper, and return AudioSegment-shaped |

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

<details> <summary>7 per-operation tools — one per public API method (click to expand)</summary>

| MCP Tool | Toggle Env Var | Description | |----------|----------------|-------------| | audio_transcriber_export | AUDIO_TRANSCRIBERTOOL | Export transcription to specified formats. | | audio_transcriber_initiate_stream | AUDIO_TRANSCRIBERTOOL | Initiate the audio input stream. | | audio_transcriber_interact | AUDIO_TRANSCRIBERTOOL | Interact with PersonaPlex server via WebSocket. | | audio_transcriber_record | AUDIO_TRANSCRIBERTOOL | Record audio for a specified duration or until stopped. | | audio_transcriber_save_stream | AUDIO_TRANSCRIBERTOOL | Save the recorded frames to a WAV file. | | audio_transcriber_stop_stream | AUDIO_TRANSCRIBERTOOL | Stop and close the audio stream. | | audio_transcriber_transcribe | AUDIO_TRANSCRIBERTOOL | Transcribe the audio file using the initialized backend. |

</details>

4 action-routed tool(s) · 7 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (intent default — the six verb-tools, granular set loaded on demand · condensed action-routed · verbose 1:1 · both). Auto-generated — do not edit.

<!-- MCP-TOOLS-TABLE:END -->

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/usage.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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MCP Configuration Examples

<!-- MCP-CONFIG-EXAMPLES:START -->

Install the connector-focused [mcp] extra. Examples use audio-transcriber[mcp] to add FastMCP / FastAPI through agent-utilities[mcp]; the required Agent Utilities core still carries epistemic-graph[full]. The [agent-runtime] extra additionally enables model orchestration.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "audio-transcriber-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "audio-transcriber[mcp]",
        "audio-transcriber-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "intent",
        "AUDIO_PROCESSINGTOOL": "True",
        "MEDIA_SIDECARTOOL": "True",
        "MISCTOOL": "True",
        "TRANSCRIBE_DIRECTORY": "/path/to/transcribe_directory",
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as GraphOS. Other launchers must omit those entries and inject the resolved values through their own runtime secret boundary.

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "audio-transcriber-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "audio-transcriber[mcp]",
        "audio-transcriber-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "AUDIO_PROCESSINGTOOL": "True",
        "MEDIA_SIDECARTOOL": "True",
        "MISCTOOL": "True",
        "TRANSCRIBE_DIRECTORY": "/path/to/transcribe_directory",
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "audio-transcriber-mcp": {
      "url": "http://localhost:8000/audio-transcriber-mcp/mcp"
    }
  }
}

Run a reviewed container image as a least-privilege stdio child (no listener or published port):

docker run -i --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges \
  --pids-limit=256 \
  --tmpfs /tmp:rw,noexec,nosuid,nodev,size=64m \
  -e TRANSPORT=stdio \
  -e MCP_TOOL_MODE=intent \
  -e AUDIO_PROCESSINGTOOL=True \
  -e MEDIA_SIDECARTOOL=True \
  -e MISCTOOL=True \
  -e TRANSCRIBE_DIRECTORY=/path/to/transcribe_directory \
  -e WHISPER_MODEL=base \
  registry.example.invalid/audio-transcriber@sha256:<digest> audio-transcriber-mcp

For containerized network HTTP, supply an authenticated TLS ingress (or direct server TLS), exact MCP_ALLOWED_HOSTS, and an exact trusted-proxy CIDR policy through the operator-owned deployment profile. The generator does not emit an unauthenticated non-loopback listener.

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

<!-- MCP-CONFIG-EXAMPLES:END --> <!-- BEGIN GENERATED: additional-deployment-options -->

Additional Deployment Options

audio-transcriber can run as a local stdio process or container, or behind a remote network boundary. The Deployment guide carries the detailed transport contract.

• Local container — launch a reviewed immutable image as a least-privilege stdio child with no listener or published port.
• Remote URL — connect through an operator-supplied authenticated HTTPS ingress. Keep its URL, outbound identity references, trust profile, and exact MCP_ALLOWED_HOSTS in AgentConfig.

<!-- END GENERATED: additional-deployment-options -->

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Configure transcription (optional)
export WHISPER_MODEL="base"
export TRANSCRIBE_DIRECTORY="/path/to/transcribe_directory"

# Run the agent server
audio-transcriber-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  audio-transcriber-mcp:
    image: example/audio-transcriber:mcp
    container_name: audio-transcriber-mcp
    hostname: audio-transcriber-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  audio-transcriber-agent:
    image: example/audio-transcriber@sha256:<digest>
    container_name: audio-transcriber-agent
    hostname: audio-transcriber-agent
    restart: always
    depends_on:
      - audio-transcriber-mcp
    env_file:
      - ../.env
    command: [ "audio-transcriber-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9014