arr-mcp 0.44.0

Arr Suite MCP Server for Agentic AI!

Arr Mcp

CLI or API | MCP | Agent

Version: 0.44.0

Documentation — Installation, deployment, usage across the API, CLI, MCP, and A2A agent interfaces, and guidance for provisioning the Arr Suite services are maintained in the official documentation.

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Table of Contents

• Overview
• Key Features
• Installation
• CLI or API
• MCP
  • Dynamic Tool Selection & Visibility
• Agent
• Environment Variables
• Security & Governance
• Contribute

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Overview

Arr Mcp is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Arr Suite MCP Server for Agentic AI!.

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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 Arr Suite MCP Server for Agentic AI! 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

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/index.md#mcp-tools.

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

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": {
    "arr-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "arr-mcp",
        "arr-mcp"
      ],
      "env": {
        "ARR_HOST": "your_arr_host_here",
        "ARR_API_KEY": "your_arr_api_key_here",
        "PVR_API_KEY": "your_pvr_api_key_here",
        "PLEX_TOKEN": "your_plex_token_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": {
    "arr-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "arr-mcp",
        "arr-mcp"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "ARR_HOST": "your_arr_host_here",
        "ARR_API_KEY": "your_arr_api_key_here",
        "PVR_API_KEY": "your_pvr_api_key_here",
        "PLEX_TOKEN": "your_plex_token_here"
      }
    }
  }
}

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

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

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name arr-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e ARR_HOST="your_value" \
  -e ARR_API_KEY="your_value" \
  -e PVR_API_KEY="your_value" \
  -e PLEX_TOKEN="your_value" \
  knucklessg1/arr-mcp:latest

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Additional Deployment Options

arr-mcp 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://arr-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 ARR_HOST="your_value"
export ARR_API_KEY="your_value"
export PVR_API_KEY="your_value"
export PLEX_TOKEN="your_value"

# Run the agent server
arr-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:
  arr-mcp-mcp:
    image: knucklessg1/arr-mcp:latest
    container_name: arr-mcp-mcp
    hostname: arr-mcp-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"

  arr-mcp-agent:
    image: knucklessg1/arr-mcp:latest
    container_name: arr-mcp-agent
    hostname: arr-mcp-agent
    restart: always
    depends_on:
      - arr-mcp-mcp
    env_file:
      - ../.env
    command: [ "arr-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9099
      - MCP_URL=http://arr-mcp-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9099:9099"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9099/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/index.md#a2a-agent.

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Environment Variables

The Arr MCP and Agent servers support extensive environment configuration. The full list of supported variables is documented below:

Variable	Description	Default	Required for
HOST	The bind host for Streamable-HTTP or SSE transport.	0.0.0.0	Server Transport
PORT	The bind port for Streamable-HTTP or SSE transport.	8000	Server Transport
TRANSPORT	MCP communication transport layer (stdio, streamable-http, sse).	stdio	Server Transport
ENABLE_OTEL	Enable OpenTelemetry logging and tracing integration.	True	Observability
EUNOMIA_TYPE	Access control policy engine type (none, embedded, remote).	none	Access Governance
EUNOMIA_POLICY_FILE	Scoped embedded policy file location.	mcp_policies.json	Access Governance
DEFAULT_AGENT_NAME	Custom name identification for the Graph Agent.	Arr Mcp	Graph Agent
ALLOWED_CLIENT_REDIRECT_URIS	Permitted client redirect URIs for authentication flows.	None	Auth Flow
AUTH_TYPE	Type of authentication used for A2A endpoints.	None	Auth Flow
SONARR_ENABLED	Toggle to enable/disable Sonarr MCP tools and client.	False	Sonarr Service
SONARR_BASE_URL	Base API URL of your Sonarr service.	None	Sonarr Service
SONARR_TOKEN	Authentication API token for Sonarr.	None	Sonarr Service
SONARR_SSL_VERIFY	Verify SSL certificates for Sonarr requests.	False	Sonarr Service
RADARR_ENABLED	Toggle to enable/disable Radarr MCP tools and client.	False	Radarr Service
RADARR_BASE_URL	Base API URL of your Radarr service.	None	Radarr Service
RADARR_TOKEN	Authentication API token for Radarr.	None	Radarr Service
RADARR_SSL_VERIFY	Verify SSL certificates for Radarr requests.	False	Radarr Service
LIDARR_ENABLED	Toggle to enable/disable Lidarr MCP tools and client.	False	Lidarr Service
LIDARR_BASE_URL	Base API URL of your Lidarr service.	None	Lidarr Service
LIDARR_TOKEN	Authentication API token for Lidarr.	None	Lidarr Service
LIDARR_SSL_VERIFY	Verify SSL certificates for Lidarr requests.	False	Lidarr Service
PROWLARR_ENABLED	Toggle to enable/disable Prowlarr MCP tools and client.	False	Prowlarr Service
PROWLARR_BASE_URL	Base API URL of your Prowlarr service.	None	Prowlarr Service
PROWLARR_TOKEN	Authentication API token for Prowlarr.	None	Prowlarr Service
PROWLARR_SSL_VERIFY	Verify SSL certificates for Prowlarr requests.	False	Prowlarr Service
BAZARR_ENABLED	Toggle to enable/disable Bazarr MCP tools and client.	False	Bazarr Service
BAZARR_BASE_URL	Base API URL of your Bazarr service.	None	Bazarr Service
BAZARR_API_KEY	Authentication API key for Bazarr.	None	Bazarr Service
BAZARR_SSL_VERIFY	Verify SSL certificates for Bazarr requests.	False	Bazarr Service
SEERR_ENABLED	Toggle to enable/disable Seerr MCP tools and client.	False	Seerr Service
SEERR_BASE_URL	Base API URL of your Seerr service.	None	Seerr Service
SEERR_API_KEY	Authentication API key for Seerr.	None	Seerr Service
SEERR_SSL_VERIFY	Verify SSL certificates for Seerr requests.	False	Seerr Service
CHAPTARR_ENABLED	Toggle to enable/disable Chaptarr MCP tools and client.	False	Chaptarr Service
CHAPTARR_BASE_URL	Base API URL of your Chaptarr service.	None	Chaptarr Service
CHAPTARR_TOKEN	Authentication API token for Chaptarr.	None	Chaptarr Service
CHAPTARR_SSL_VERIFY	Verify SSL certificates for Chaptarr requests.	False	Chaptarr Service

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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

Installation

Install the Python package locally:

# Using uv (highly recommended)
uv pip install arr-mcp[all]

# Using standard pip
python -m pip install arr-mcp[all]

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Usage & Quick Start

To launch and run arr-mcp services:

1. Launching the MCP Server

Launch the MCP server in standard I/O mode (ideal for IDEs):

arr-mcp

Or launch it as a Streamable-HTTP server on port 8000:

arr-mcp --transport streamable-http --port 8000

2. Running the Graph Agent Server

Start the interactive Pydantic AI Graph Agent CLI with OIDC token delegation and Eunomia policies:

arr-agent --provider openai --model-id gpt-4o

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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 and agent servers, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the Python API clients, the CLI
Backing Platform	provision the Arr Suite services with Docker
Overview	ecosystem role, enterprise readiness, configuration
Concepts	concept registry (CONCEPT:ARR-*)

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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