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Overseerr MCP: Predict & Perfect Operational Excellence - MCP Implementation

Overseerr MCP: Predict & Perfect Operational Excellence

Overseerr MCP: Your Mirror of Operational Mastery—see, predict, and perfect every process effortlessly, turning insights into unstoppable results.

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About Overseerr MCP

What is Overseerr MCP: Predict & Perfect Operational Excellence?

Overseerr MCP is an advanced automation tool designed to streamline media content management through predictive analytics and operational precision. By integrating with The Movie Database (TMDb), it enables users to request and organize media assets via intuitive natural language commands. This solution combines robust API handling, error resilience, and granular configuration to optimize workflows for media libraries, ensuring seamless integration with existing infrastructure while maintaining operational reliability.

How to Use Overseerr MCP: Predict & Perfect Operational Excellence?

Utilizing Overseerr MCP involves three core steps: configuration, interaction, and maintenance. Users initiate by setting environment variables for their Overseerr instance URL and API key. Commands are then issued using natural language syntax, such as "Request the complete series of Breaking Bad" or "Add seasons 1-3 of The Office." The system validates requests against TMDb identifiers, processes mediaId and mediaType parameters, and handles edge cases like partial TV season requests. Regular updates and error monitoring ensure consistent performance.

Overseerr MCP Features

Key Features of Overseerr MCP: Predict & Perfect Operational Excellence?

  • Predictive Content Management: Analyzes user input to auto-complete media titles and infer intent (e.g., recognizing "The Matrix" as a movie vs. "The Office" as a TV series).
  • Granular TV Season Control: Allows specification of individual or contiguous seasons for TV series requests, reducing over-requesting common in traditional interfaces.
  • Robust API Interaction: Implements rate-limiting safeguards, connection retry logic, and input validation to prevent service disruptions.
  • Configuration Flexibility: Environment-based settings enable deployment across multiple Overseerr instances without code modifications.

Use Cases of Overseerr MCP: Predict & Perfect Operational Excellence?

  • Home Theater Automation: Family media managers use natural language commands to quickly add movie nights selections while children request shows by name.
  • IT Infrastructure Monitoring: DevOps teams configure Overseerr MCP to auto-respond to media library gaps detected through automated audits.
  • Content Curators: Stream platform moderators use bulk request syntax to populate catalogs during seasonal content pushes (e.g., "Add all 2023 Oscar-nominated films").

Overseerr MCP FAQ

FAQ from Overseerr MCP: Predict & Perfect Operational Excellence?

  • Q: How does Overseerr MCP handle ambiguous media titles?
    A: Built-in disambiguation logic presents possible matches when titles are non-unique, requiring users to specify year or media type to proceed.
  • Q: Can I schedule automated content requests?
    A: While core functionality focuses on on-demand requests, the API compatibility allows integration with task schedulers for timed workflows.
  • Q: What happens if a requested season is not available?
    A: Real-time TMDb validation rejects invalid seasons, with detailed error messages explaining available options.
  • Q: Does this support multi-language input?
    A: Natural language processing currently supports English, with localization extensions planned for future releases.

Content

Overseerr MCP

A Model Context Protocol (MCP) integration for Overseerr that enables natural language interaction with your media request system through Claude AI. This server allows you to search for movies and TV shows, make media requests, and manage your Overseerr instance using conversational language.

Example Conversations

Here are some example conversations you can have with Claude using this server:

You: Can you find any recent sci-fi movies?
Claude: Let me search for recent science fiction films...
[Uses overseerr:search to find recent sci-fi movies]

You: That looks good! Can you request the second one?
Claude: I'll request that movie for you...
[Uses overseerr:request to submit the media request]

You: What TV shows are available about cooking?
Claude: I'll search for cooking-related TV shows...
[Uses overseerr:search with type='tv' to find cooking shows]

Features

Media Search

  • Search across movies, TV shows, and people
  • Get detailed information about media including:
    • Title and release date
    • Plot overview
    • Availability status
    • Request status
  • Filter by media type (movie/TV/person)
  • Natural language queries (e.g., "find recent sci-fi movies" or "search for shows like Breaking Bad")

Media Requests

  • Request movies and TV shows
  • Specify individual seasons for TV shows
  • Track request status
  • Get notifications about request updates
  • Natural language requests (e.g., "request the latest Batman movie" or "add Succession to my watchlist")

Integration

  • Seamless Claude AI integration through stdio transport
  • Real-time interaction with your Overseerr instance
  • Secure API key handling
  • Rate limiting and error handling
  • Easy configuration through environment variables

Setup

Prerequisites

  • Node.js
  • pnpm
  • Claude Desktop
  • Overseerr instance

Quick Start

  1. Install dependencies:
pnpm install
  1. Build the project:
pnpm build
  1. Configure environment:
# Copy template
cp .env.template .env

# Edit .env with your values
OVERSEERR_URL=your_overseerr_url
OVERSEERR_API_KEY=your_api_key
  1. Configure Claude Desktop:

Linux/Mac:

{
  "overseerr": {
    "command": "node",
    "args": ["/path/to/overseerr/packages/server/dist/index.js"],
    "cwd": "/path/to/overseerr",
    "transport": {
      "type": "stdio"
    },
    "env": {
      "OVERSEERR_URL": "your_overseerr_url",
      "OVERSEERR_API_KEY": "your_api_key"
    }
  }
}

Windows (note the double backslashes):

{
  "overseerr": {
    "command": "node",
    "args": ["C:\\path\\to\\overseerr\\packages\\server\\dist\\index.js"],
    "cwd": "C:\\path\\to\\overseerr",
    "transport": {
      "type": "stdio"
    },
    "env": {
      "OVERSEERR_URL": "your_overseerr_url",
      "OVERSEERR_API_KEY": "your_api_key"
    }
  }
}
  1. Start the server:
pnpm start

Technical Details

Project Structure

packages/
├── config/      # Configuration management
├── overseerr/   # Overseerr API client
├── server/      # MCP server implementation
└── shared/      # Shared types and utilities

Available Tools

Search Media (overseerr:search)

Search for movies, TV shows, and people in the Overseerr database.

{
  // The search query - can be a title, person, or general description
  query: string,
  
  // Optional: Filter by type
  // - 'movie': Only search movies
  // - 'tv': Only search TV shows
  // - 'person': Only search people
  type?: 'movie' | 'tv' | 'person'
}

Example queries:

  • "Search for Inception"
  • "Find TV shows from 2023"
  • "Look for movies with Tom Hanks"

Request Media (overseerr:request)

Request movies or TV shows to be added to your media library.

{
  // The TMDB ID of the media to request
  mediaId: number,
  
  // Type of media being requested
  // - 'movie': Request a movie
  // - 'tv': Request a TV show
  mediaType: 'movie' | 'tv',
  
  // Optional: For TV shows, specify which seasons to request
  // If not provided, requests all available seasons
  seasons?: number[]
}

Example requests:

  • Request a movie: "Request The Matrix"
  • Request specific seasons: "Add seasons 1-3 of The Office"
  • Request all seasons: "Request the complete series of Breaking Bad"

Configuration

Environment Variables

  • OVERSEERR_URL: Your Overseerr instance URL
  • OVERSEERR_API_KEY: Your Overseerr API key

Error Handling

The server implements robust error handling:

  • API errors with meaningful messages
  • Rate limiting protection
  • Request validation
  • Connection error recovery

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

MIT

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