FastMCP Server for Overseerr: AI Application Integration Through MCP Protocol

Overview: What is FastMCP Server for Overseerr?

FastMCP Server for Overseerr is an MCP (Model Context Protocol) server designed to interact with the Overseerr API, enabling seamless management of movie and TV show requests. This server allows AI applications like Claude Desktop, Continue, Cursor, and others to connect to Overseer using a standardized protocol, enhancing integration and functionality.

🔧 Core Features & MCP Capabilities

The FastMCP Server implements several tools that facilitate communication with the Overseerr API. These tools provide a rich set of functionalities for interacting with movie and TV requests, managing libraries, searching media, and fetching user information:

• overseerr_status: Get the status of the Overseerr server.
• overseerr_movie_requests: Get a paginated list of movie requests based on various filters such as status, start_date, take, and skip.
• overseerr_tv_requests: Similar to movie_requests but for TV show requests.
• overseerr_request_movie_to_library: Submit an individual movie request by providing its TMDB ID, library name, and user display name. The available libraries and user names are fetched at startup.
• overseerr_request_tv_to_library: Like request_movie_to_library, but includes the ability to specify seasons for TV shows.
• overseerr_search_media: Search for movies and TV shows in Overseerr using a query and optional page number.
• overseerr_get_available_libraries: Get information on configured Sonarr (TV) and Radarr (Movie) servers.
• overseerr_get_users: Fetch the list of users configured in Overseerr.

These features enable AI applications to manage media requests more effectively, track user activity, and integrate with popular streaming services like Sonarr and Radarr.

⚙️ MCP Architecture & Protocol Implementation

The FastMCP Server utilizes the fastmcp library, which follows the Model Context Protocol (MCP) standards. This protocol ensures that AI applications can interact seamlessly with diverse data sources and tools. The server architecture is designed to handle requests in real-time, providing responses quickly and accurately.

MCP Protocol Flow Diagram

The following diagram illustrates the flow of communication between an AI application running a specific MCP client and the FastMCP Server for Overseerr:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram highlights the flow of data and commands between the AI application, MCP Client, MCP Protocol, and finally, the FastMCP Server for Overseerr. This standardized communication ensures compatibility across different AI platforms and data sources.

🚀 Getting Started with Installation

To get started with deploying the FastMCP Server, follow these steps:

1. Prerequisites: Ensure you have Python >= 3.12 installed, along with uv (installation instructions: https://github.com/astral-sh/uv), and an Overseerr instance running.

2. Clone the Repository:

   git clone https://github.com/ptbsare/overseerr-mcp-server.git
   cd overseerr-mcp-server
   

3. Set Up the Environment:

   • Using Client Configurations: Modify your MCP client's configuration file (e.g., claude_desktop_config.json). Since the package is not published, you need to run it from the source directory using uv.

      {
        "mcpServers": {
          "overseerr-mcp-server": {
            "command": "uv",
            "args": [
               "run",
               "--directory",
               "/path/to/overseerr-mcp-server", // Replace with actual path
               "overseerr-mcp-server"
              ],
            "env": {
              "OVERSEERR_API_KEY": "<your_api_key_here>",
              "OVERSEERR_URL": "<your_overseerr_url>"
            }
          }
        }
      }
     

   • Using a .env File: Create a .env file in the root directory of the cloned repository (/path/to/overseerr-mcp-server) with the following content:

     # Required: Your Overseerr API Key
     OVERSEERR_API_KEY=your_api_key_here
     
     # Required: The URL of your Overseerr instance (e.g., http://localhost:5055)
     OVERSEERR_URL=your_overseerr_url_here
     

4. Run the Server:

   uv run overseerr-mcp-server
   

   Ensure your virtual environment is active and that .env file exists or environment variables are set in config.

💡 Key Use Cases in AI Workflows

The FastMCP Server for Overseerr can be integrated into various AI workflows, enhancing functionality and user experience. Here are two realistic use cases:

1. Real-time Media Request Management: An AI application could use the overseerr_request_movie_to_library or overseerr_request_tv_to_library tools to programmatically submit media requests based on user preferences or trending content.

2. Automated Media Library Updates: By leveraging the search_media, get_available_libraries, and request_tv_to_library/request_movie_to_library tools, an AI application can automate the process of updating a media library with new content as it is released.

Example MCP Protocol Interaction:

{
  "mcpServers": {
    "overseerr-mcp-server": {
      "command": "uv",
      "args": [
         "run",
         "--directory",
         "/path/to/overseerr-mcp-server", // Replace with actual path
         "overseerr-mcp-server"
       ],
      "env": {
        "OVERSEERR_API_KEY": "<your_api_key_here>",
        "OVERSEERR_URL": "<your_overseerr_url>"
      }
    }
  }
}

This setup ensures the FastMCP Server for Overseerr is ready to receive and process requests from AI applications, enabling dynamic media management.

🔌 Integration with MCP Clients

The FastMCP Server is compatible with various MCP clients such as Claude Desktop, Continue, Cursor, and others. The following table provides a compatibility matrix highlighting where it supports different features:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The performance and compatibility of the FastMCP Server for Overseerr have been tested across different environments. The server ensures real-time data exchange with minimal latency, making it suitable for both small-scale and large-scale AI applications.

Example MCP Configuration Code Sample:

{
  "mcpServers": {
    "overseerr-mcp-server": {
      "command": "uv",
      "args": [
         "run",
         "--directory",
         "/path/to/overseerr-mcp-server", // Replace with actual path
         "overseerr-mcp-server"
       ],
      "env": {
        "OVERSEERR_API_KEY": "<your_api_key_here>",
        "OVERSEERR_URL": "<your_overseerr_url>"
      }
    }
  }
}

Emphasizing AI Application Integration

The FastMCP Server for Overseerr enhances AI applications by seamlessly integrating with media management tools like Overseerr. By leveraging the MCP protocol, developers can build more robust and flexible systems that dynamically manage media requests based on user preferences.

Real-World AI Workflow Scenario:

Imagine a scenario where an AI application uses the overseerr_request_movie_to_library tool to submit a request for a new movie as soon as it is released. This automated process ensures that users can access media promptly, enhancing their experience and satisfaction.

Conclusion

The FastMCP Server for Overseerr offers a powerful solution for integrating AI applications with media management tools like Overseerr. By following the installation steps and leveraging the various tools provided, developers can build sophisticated systems for managing media requests in real-time. This server is an essential component for any AI application looking to enhance its functionality through MCP protocol integration.

For more information or support, refer to the official FastMCP documentation or community forums.