audio-transcriber 0.35.0

Transcribe your .wav .mp4 .mp3 .flac files to text or record your own audio!

Audio Transcriber

CLI or API | MCP | Agent

Version: 0.35.0

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 Tool	Toggle Env Var	Description
health_check	MISCTOOL
transcribe_audio	AUDIO_PROCESSINGTOOL	Transcribes audio from a provided file or by recording from the microphone.

2 action-routed tools (default MCP_TOOL_MODE=condensed). Each is enabled unless its toggle is set false; set MCP_TOOL_MODE=verbose (or both) for the 1:1 per-operation surface. Auto-generated — do not edit.

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

Install the slim [mcp] extra. All examples below install audio-transcriber[mcp] — the MCP-server extra that pulls only the FastMCP / FastAPI tooling (agent-utilities[mcp]). It deliberately excludes the heavy agent runtime (the epistemic-graph engine, pydantic-ai, dspy, llama-index, tree-sitter), so uvx/container installs are dramatically smaller and faster. Use the full [agent] extra only when you need the integrated Pydantic AI agent (see Installation).

stdio Transport (Recommended for local IDEs e.g., Cursor, Claude Desktop)

Configure your IDE's mcp.json to launch the MCP server via uvx:

{
  "mcpServers": {
    "audio-transcriber": {
      "command": "uvx",
      "args": [
        "--from",
        "audio-transcriber[mcp]",
        "audio-transcriber-mcp"
      ],
      "env": {
        "AUDIO_TRANSCRIPTOR_API_KEY": "your_audio_transcriptor_api_key_here",
        "LANGSMITH_DEFAULT_SYSTEM_PROMPT": "your_langsmith_default_system_prompt_here",
        "OPENROUTER_API_KEY": "your_openrouter_api_key_here"
      }
    }
  }
}

Streamable-HTTP Transport (Recommended for production deployments)

Configure your client's mcp.json to launch the Streamable-HTTP server via uvx with explicit host and port definition:

{
  "mcpServers": {
    "audio-transcriber": {
      "command": "uvx",
      "args": [
        "--from",
        "audio-transcriber[mcp]",
        "audio-transcriber-mcp"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "AUDIO_TRANSCRIPTOR_API_KEY": "your_audio_transcriptor_api_key_here",
        "LANGSMITH_DEFAULT_SYSTEM_PROMPT": "your_langsmith_default_system_prompt_here",
        "OPENROUTER_API_KEY": "your_openrouter_api_key_here"
      }
    }
  }
}

Alternatively, connect to a pre-deployed remote or local Streamable-HTTP instance:

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

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name audio-transcriber-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e AUDIO_TRANSCRIPTOR_API_KEY="your_value" \
  -e LANGSMITH_DEFAULT_SYSTEM_PROMPT="your_value" \
  -e OPENROUTER_API_KEY="your_value" \
  knucklessg1/audio-transcriber:mcp

The :mcp tag is the slim MCP-server image (built from docker/Dockerfile --target mcp, installing audio-transcriber[mcp]). The default :latest tag is the full agent image (--target agent, audio-transcriber[agent]) which also bundles the Pydantic AI agent and the epistemic-graph engine — use it when you run audio-transcriber-agent (the agent), not just the MCP server. See Container images.

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Additional Deployment Options

audio-transcriber can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at http://audio-transcriber-mcp.arpa/mcp using the "url" key.

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:

# Set credentials
export AUDIO_TRANSCRIPTOR_API_KEY="your_value"
export LANGSMITH_DEFAULT_SYSTEM_PROMPT="your_value"
export OPENROUTER_API_KEY="your_value"

# 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: knucklessg1/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: knucklessg1/audio-transcriber:latest
    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
      - MCP_URL=http://audio-transcriber-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9014:9014"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9014/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/agent.md.

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Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

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Environment Variables

Every variable the server reads, grouped by purpose.

Transcription / Credentials

Variable	Description	Default
AUDIO_TRANSCRIPTOR_API_KEY	API key for the transcription backend	—
OPENROUTER_API_KEY	OpenRouter API key (LLM provider)	—
LANGSMITH_DEFAULT_SYSTEM_PROMPT	Default system prompt for LangSmith tracing	—
WHISPER_MODEL	Local OpenAI Whisper model (e.g. base, tiny, small)	base

MCP server / transport

Variable	Description	Default
TRANSPORT	stdio, streamable-http, or sse	stdio
HOST	Bind host (HTTP transports)	0.0.0.0
PORT	Bind port (HTTP transports)	8000
MCP_TOOL_MODE	Tool surface: condensed, verbose, or both	condensed
MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS	Comma-separated tool allow/deny list	—
MCP_ENABLED_TAGS / MCP_DISABLED_TAGS	Comma-separated tag allow/deny list	—
DEBUG	Verbose logging	False
PYTHONUNBUFFERED	Unbuffered stdout (recommended in containers)	1

Tool toggles

Each action-routed tool can be disabled individually via its toggle env var (set to false). See the Available MCP Tools table above for the authoritative names.

Variable	Description	Default
MISCTOOL	Toggle the miscellaneous / health-check tool	True
AUDIO_PROCESSINGTOOL	Toggle the audio-processing (transcription) tool	True

Telemetry & governance

Variable	Description	Default
ENABLE_OTEL	Enable OpenTelemetry export	True
OTEL_EXPORTER_OTLP_ENDPOINT	OTLP collector endpoint	—
OTEL_EXPORTER_OTLP_PUBLIC_KEY / OTEL_EXPORTER_OTLP_SECRET_KEY	OTLP auth keys	—
OTEL_EXPORTER_OTLP_PROTOCOL	OTLP protocol (e.g. http/protobuf)	—
EUNOMIA_TYPE	Authorization mode: none, embedded, remote	none
EUNOMIA_POLICY_FILE	Embedded policy file	mcp_policies.json
EUNOMIA_REMOTE_URL	Remote Eunomia server URL	—

Agent CLI (full [agent] runtime only)

Variable	Description	Default
MCP_URL	URL of the MCP server the agent connects to	http://localhost:8000/mcp
PROVIDER	LLM provider (e.g. openai)	openai
MODEL_ID	Model id (e.g. gpt-4o)	gpt-4o
ENABLE_WEB_UI	Serve the AG-UI web interface	True

See .env.example for a copy-paste starting point.

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Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
audio-transcriber[mcp]	Slim MCP server only (agent-utilities[mcp] — FastMCP/FastAPI)	You only run the MCP server (smallest install / image)
audio-transcriber[agent]	Full agent runtime (agent-utilities[agent,logfire] — Pydantic AI + the epistemic-graph engine)	You run the integrated agent
audio-transcriber[all]	Everything (mcp + agent)	Development / both surfaces

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "audio-transcriber[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "audio-transcriber[agent]"

# Everything (development)
uv pip install "audio-transcriber[all]"      # or: python -m pip install "audio-transcriber[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
knucklessg1/audio-transcriber:mcp	--target mcp	audio-transcriber[mcp] — slim, no engine/pydantic-ai/dspy/llama-index/tree-sitter	audio-transcriber-mcp
knucklessg1/audio-transcriber:latest	--target agent (default)	audio-transcriber[agent] — full agent runtime + epistemic-graph engine	audio-transcriber-agent

docker build --target mcp   -t knucklessg1/audio-transcriber:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/audio-transcriber:latest docker/   # full agent

docker/mcp.compose.yml runs the slim :mcp server; docker/agent.compose.yml runs the agent (:latest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

The full agent ([agent] / :latest) embeds the epistemic-graph engine (pulled in transitively via agent-utilities[agent]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide. The slim [mcp] server does not require the database.

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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 executing local checks before submitting pull requests:

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

Deploy with agent-os-genesis

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy audio-transcriber with agent-os-genesis".

Install mode	Command
Bare-metal, prod (PyPI)	uvx audio-transcriber-mcp · or uv tool install audio-transcriber
Bare-metal, dev (editable)	uv pip install -e ".[all]" · or pip install -e ".[all]"
Container, prod	deploy knucklessg1/audio-transcriber:latest via docker-compose / swarm / podman / podman-compose / kubernetes
Container, dev (editable)	deploy docker/compose.dev.yml (source-mounted at /src; edits live on restart)

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.