Audio Transcriber

CLI or API | MCP

Documentation — Installation, deployment, and usage across the CLI, Python API, 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 the operator's .wav .mp4 .mp3 .flac files to text or record the operator's 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: Complete support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• 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 the operator's .wav .mp4 .mp3 .flac files to text or record the operator's own audio! API. The operator 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 use 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.

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)

7 per-operation tools — one per public API method (click to expand)

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.

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.

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 the operator to restrict the set of exposed tools to prevent blowing up the LLM's context window.

The operator 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 the operator's transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are provided, 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

Install the connector-focused [mcp] extra. Examples use audio-transcriber[mcp] to add FastMCP / FastAPI through agent-connector-sdk; the required Agent Connector SDK 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 Arbora. 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.

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.

Agent

Configure transcription (optional)

export WHISPER_MODEL="base" export TRANSCRIBE_DIRECTORY="/path/to/transcribe_directory"

Connector-focused MCP server (includes the shared graph engine)

uv pip install "audio-transcriber[mcp]"

Everything (development)

uv pip install "audio-transcriber[all]" # or: python -m pip install "audio-transcriber[all]"

### Container images (`:mcp`)

One multi-stage `docker/Dockerfile` builds the MCP-only image (`--target mcp`):

| Image tag | Build target | Contents | Entrypoint |
|-----------|--------------|----------|------------|
| `example/audio-transcriber:mcp` | `--target mcp` | `audio-transcriber[mcp]` — **connector-focused**, includes `epistemic-graph[full]`; no model-orchestration stack | `audio-transcriber-mcp` |

```bash
docker build --target mcp   -t example/audio-transcriber:mcp    docker/   # connector-focused MCP server

agent (immutable agent digest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

Agent Connector SDK core dependency (epistemic-graph[full]). The [mcp] extra keeps deployments can use the bundled engine. For production or shared state, run epistemic-graph as a dedicated database service and configure the runtime to use it. Deployment recipes (single-node + Raft HA), connection configuration, and architecture diagrams are documented in the epistemic-graph deployment guide.

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Repository Owners

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Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, extras, prebuilt Docker image
Deployment	run the MCP server and agent, Compose, Caddy + Technitium, env config
Usage	the MCP tool, the AudioTranscriber API, the CLI
Overview	capability summary and ecosystem role
Concepts	concept registry (CONCEPT:AUDIO-*)

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Contribute

Contributions are welcome! Please ensure code quality by running local checks before submitting pull requests:

• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Ran test suites using pytest

Deploy with agent-connector-sdk-deployment

Provision this package with the consolidated agent-connector-sdk-deployment workflow. It selects an installed-package, editable-source, or immutable-container path; records only runtime secret and TLS-profile references in AgentConfig; and runs doctor, registration, policy, observability, and rollback gates. Ask your agent to "deploy audio-transcriber with agent-connector-sdk-deployment".

Install mode	Command
Installed package	uv tool install "audio-transcriber[mcp]", then run audio-transcriber-mcp
Immutable container	deploy registry.example.invalid/audio-transcriber@sha256:<digest> through the operator-selected orchestrator

The repository embeds no deployment profile, credential value, certificate path, or environment-specific endpoint. Supply those at runtime through AgentConfig and the configured secret provider.

Governed capability contract

This package ships a compact canonical skill surface with specialist procedures kept as referenced workflows. The current MCP tools, skill metadata, connector_manifest.yml, ontology, mappings, shapes, fixtures, migrations, tool-schema fingerprints, and certification metadata form one versioned capability contract. Validate them together; do not rely on stale tool names or historical per-task skill wrappers.

Runtime endpoints, credentials, certificate trust, tenant identity, retention, and observability policy are deployment inputs and are never packaged values. See Configuration, trust, and privacy before enabling a network transport, connector ingestion, Arbora delegation, or trace export.