Media Downloader

CLI or API | MCP

PyPI - Version MCP Server PyPI - Downloads GitHub Repo stars GitHub forks GitHub contributors PyPI - License GitHub GitHub last commit (by committer) GitHub pull requests GitHub closed pull requests GitHub issues GitHub top language GitHub language count GitHub repo size GitHub repo file count (file type) PyPI - Wheel PyPI - Implementation

Version: 4.4.0

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

Overview

Media Downloader is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Download audio/videos from the internet! Host an MCP Server for Agentic AI to download videos!.

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.
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.

CLI or API

This agent wraps the Download audio/videos from the internet! Host an MCP Server for Agentic AI to download videos! 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.

MCP

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

Available MCP Tools

Condensed action-routed tools (`MCP_TOOL_MODE=condensed`)

MCP Tool	Toggle Env Var	Description
`build_watch_skill`	—	Write a video-built skill, or append another video's findings to one.
`download_media`	—	Download video or audio from supported sites (YouTube, Rumble, etc.).
`list_watch_skills`	—	List video-built skills under a root and the videos each already has.
`watch_media`	—	Watch a video: download it with captions and extract key frames.

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
`media_downloader_download_all`	`MEDIA_DOWNLOADERTOOL`	Invoke the download_all operation.
`media_downloader_download_video`	`MEDIA_DOWNLOADERTOOL`	Invoke the download_video operation.
`media_downloader_get_channel_videos`	`MEDIA_DOWNLOADERTOOL`	Invoke the get_channel_videos operation.
`media_downloader_open_file`	`MEDIA_DOWNLOADERTOOL`	Invoke the open_file operation.
`media_downloader_progress_hook`	`MEDIA_DOWNLOADERTOOL`	Invoke the progress_hook operation.
`media_downloader_set_progress_callback`	`MEDIA_DOWNLOADERTOOL`	Invoke the set_progress_callback operation.
`media_downloader_watch`	`MEDIA_DOWNLOADERTOOL`	Download a video with its captions and extract scene-change key frames.

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 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.

MCP Configuration Examples

Install the connector-focused [mcp] extra. Examples use media-downloader[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": {
    "media-downloader-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "media-downloader[mcp]",
        "media-downloader-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "intent",
        "MEDIA_DOWNLOADER_ALLOW_PRIVATE_HOSTS": "media.internal.example",
        "MEDIA_DOWNLOADER_MAX_WORKERS": "4",
        "MEDIA_DOWNLOADER_OUTPUT_ROOT": "/data/media",
        "WORKSPACE_PATH": "/workspace"
      }
    }
  }
}

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": {
    "media-downloader-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "media-downloader[mcp]",
        "media-downloader-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "MEDIA_DOWNLOADER_ALLOW_PRIVATE_HOSTS": "media.internal.example",
        "MEDIA_DOWNLOADER_MAX_WORKERS": "4",
        "MEDIA_DOWNLOADER_OUTPUT_ROOT": "/data/media",
        "WORKSPACE_PATH": "/workspace"
      }
    }
  }
}

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

{
  "mcpServers": {
    "media-downloader-mcp": {
      "url": "http://localhost:8000/media-downloader-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 MEDIA_DOWNLOADER_ALLOW_PRIVATE_HOSTS=media.internal.example \
  -e MEDIA_DOWNLOADER_MAX_WORKERS=4 \
  -e MEDIA_DOWNLOADER_OUTPUT_ROOT=/data/media \
  -e WORKSPACE_PATH=/workspace \
  registry.example.invalid/media-downloader@sha256:<digest> media-downloader-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

media-downloader 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

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

Running the Agent CLI

To start the interactive command-line agent:

# Run the agent server
media-downloader-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

version: '3.8'

services:
  media-downloader-mcp:
    image: example/media-downloader@sha256:<digest>
    container_name: media-downloader-mcp
    hostname: media-downloader-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"

    image: example/media-downloader@sha256:<digest>
    container_name: media-downloader-agent
    hostname: media-downloader-agent
    restart: always
    depends_on:
      - media-downloader-mcp
    env_file:
      - ../.env
    command: [ "media-downloader-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9000
      - MCP_URL=http://media-downloader-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9000:9000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9000/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/deployment.md.

Security & Governance

Built directly upon the enterprise-ready agent-connector-sdk 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	Feature	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

Environment Variables

Package environment variables

Variable	Example	Description
`HOST`	`0.0.0.0`
`PORT`	`8000`
`TRANSPORT`	`stdio`	options: stdio, streamable-http, sse
`ENABLE_OTEL`	`True`
`OTEL_EXPORTER_OTLP_ENDPOINT`	`http://localhost:8080/api/public/otel`
`OTEL_EXPORTER_OTLP_PUBLIC_KEY_REF`	`pk-...`
`OTEL_EXPORTER_OTLP_SECRET_KEY_REF`	`sk-...`
`OTEL_EXPORTER_OTLP_PROTOCOL`	`http/protobuf`
`EUNOMIA_TYPE`	`none`	options: none, embedded, remote
`EUNOMIA_POLICY_FILE`	`mcp_policies.json`
`EUNOMIA_REMOTE_URL`	`http://eunomia-server:8000`
`MEDIA_DOWNLOADER_MCP_IMAGE`	`knucklessg1/media-downloader@sha256:<digest>`	Required by the hardened compose profiles — no floating tag is assumed. Point these at an immutable digest of a reviewed image before running `docker compose up`.
`MEDIA_DOWNLOADER_AGENT_IMAGE`	`knucklessg1/media-downloader@sha256:<digest>`
`MEDIA_DOWNLOADER_OUTPUT_ROOT`	`/data/media`	Every download is written beneath this root; a path that escapes it is rejected. Falls back to WORKSPACE_PATH, then ~/Downloads.
`WORKSPACE_PATH`	`/workspace`
`MEDIA_DOWNLOADER_ALLOW_PRIVATE_HOSTS`	`media.internal.example`	Exact hostnames allowed to resolve to a private address. Downloads are restricted to publicly routable hosts otherwise; no wildcards are accepted.
`MEDIA_DOWNLOADER_MAX_WORKERS`	`4`	Parallel download workers, clamped to 1-4.

Inherited agent-connector-sdk variables (apply to every connector)

Variable	Example	Description
`MCP_TOOL_MODE`	`intent`	Tool surface: `intent` \| `condensed` \| `verbose` \| `both`
`MCP_ENABLED_TOOLS`	—	Comma-separated tool allow-list
`MCP_DISABLED_TOOLS`	—	Comma-separated tool deny-list
`MCP_ENABLED_TAGS`	—	Comma-separated tag allow-list
`MCP_DISABLED_TAGS`	—	Comma-separated tag deny-list
`MCP_CLIENT_AUTH`	—	Outbound MCP child auth: `oidc-client-credentials` \| `basic` \| `none`
`OIDC_CLIENT_ID`	—	OIDC client id (service-account auth)
`OIDC_CLIENT_SECRET_REF`	`secret://identity/oidc-client-secret`	Runtime secret reference for the OIDC service account
`MCP_BASIC_AUTH_USERNAME`	—	HTTP Basic username (`MCP_CLIENT_AUTH=basic`)
`MCP_BASIC_AUTH_PASSWORD_REF`	`secret://identity/mcp-basic-password`	Runtime secret reference for HTTP Basic auth (`MCP_CLIENT_AUTH=basic`)
`DEBUG`	`False`	Verbose logging
`PYTHONUNBUFFERED`	`1`	Unbuffered stdout (recommended in containers)
`MCP_URL`	`http://localhost:8000/mcp`	URL of the MCP server the agent connects to
`PROVIDER`	—	Operator-configured LLM provider for the agent
`MODEL_ID`	—	Operator-configured model id for the agent
`ENABLE_WEB_UI`	`True`	Serve the AG-UI web interface

17 package + 16 inherited variable(s). Auto-generated from .env.example + the shared agent-connector-sdk set — do not edit.

Every variable the server reads. A local template is supplied inside .env.example — copy it to .env and adjust as needed.

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	—
`PYTHONUNBUFFERED`	Unbuffered stdout (recommended in containers)	`1`

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`

Installation

Pick the extra that matches what the operator want to run:

Extra	Installs	Use when
`media-downloader[mcp]`	Connector-focused MCP server (`agent-connector-sdk` — FastMCP/FastAPI + `epistemic-graph[full]`)	The operator only run the MCP server (smallest install / image)
`media-downloader[all]`	Everything (`mcp` + `agent` + `logfire`)	Development / both surfaces

# Connector-focused MCP server (includes the shared graph engine)
uv pip install "media-downloader[mcp]"

# Agent runtime (adds model orchestration to the shared graph engine)

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

Container images (`:mcp`)

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

Image tag	Build target	Contents	Entrypoint
`knucklessg1/media-downloader:mcp`	`--target mcp`	`media-downloader[mcp]` — connector-focused, includes `epistemic-graph[full]`; no model-orchestration stack	`media-downloader-mcp`

docker build --target mcp   -t knucklessg1/media-downloader:mcp   docker/   # connector-focused MCP server

agent (default build) with a co-located :mcp sidecar. For an immutable production digest, tag and pin the operator's own build as shown in the hardened docker run pattern above.

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 to share one knowledge graph across multiple agents — run epistemic-graph as its own dedicated database service and configure the runtime to use it instead of the bundled engine. Deployment recipes (single-node + Raft HA), connection configuration, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide.

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, the agent server, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the `MediaDownloader` Python API, the CLI
Overview	ecosystem role, enterprise readiness, architecture
Concepts	concept registry (`CONCEPT:MDLD-*`)

AGENTS.md is the canonical contributor/agent guidance.

Repository Owners

GitHub followers GitHub User's stars

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

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.

Development Status
- 5 - Production/Stable
Environment
- Console
License
- Public Domain
Operating System
- POSIX :: Linux
Programming Language
- Python :: 3

Metadata

Development Status
- 5 - Production/Stable
Environment
- Console
License
- Public Domain
Operating System
- POSIX :: Linux
Programming Language
- Python :: 3

Release files for media-downloader 4.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for media-downloader 4.4.0
File	Interpreter	ABI	Platform	Reset
media_downloader-4.4.0-py3-none-any.whl 81.3 kB Oct 18, 2026	Python 3	none	any	Details

Release files / media_downloader-4.4.0-py3-none-any.whl

Download URL	media_downloader-4.4.0-py3-none-any.whl
Size	81.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`6da298eb10045092eb328fac244604b2fe18048e47eb689f52b353837de93309`
BLAKE2b-256 checksum How to use checksums	`c8f37d12d659cba791579f394a8c6b3983761738b8e5a73b36027d5e384a4591`
Upload date	Oct 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.4`