Overseerr MCP Server: A Seamless Integration Hub for AI Applications

Overview: What is Overseerr MCP Server?

The Overseerr MCP Server acts as an intelligent bridge between Claude Desktop and other AI applications, and your media management system, Overseerr. This server leverages the Model Context Protocol (MCP) to facilitate natural language interactions with your media request system through Claude AI. It allows users to search for movies and TV shows, make media requests, and manage their Overseerr instance using conversational language.

🔧 Core Features & MCP Capabilities

The Overseerr MCP Server is designed to provide robust MPC capabilities that enhance the interaction between AI applications and media management systems. These features include:

Media Search: Users can search for movies, TV shows, or people in your database with natural language queries.
Media Requests: Make requests to add specific media items to your library using conversational language.
Real-time Interaction: Seamless communication with Overseerr through Claude Desktop over the stdio transport protocol.
Configurable Environment Variables: Easy setup and customization via environment variables, ensuring flexibility in integration.

⚙️ MCP Architecture & Protocol Implementation

The Overseerr MCP Server implements key aspects of the Model Context Protocol to ensure seamless operations. The following diagram illustrates the flow:

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 protocol allows AI applications to interact with the data source (Overseerr) through standardized commands and responses. The server processes these interactions and translates them into actionable requests or data fetches for Overseerr.

Key Features of MCP Implementation

Secure API Key Handling: Ensures that sensitive information is protected during interaction.
Rate Limiting & Error Handling: Prevents overloading the system while providing meaningful error messages to users.
Natural Language Processing (NLP): Enables understanding and parsing complex conversational queries.

🚀 Getting Started with Installation

To get started, follow these steps to set up the Overseerr MCP Server:

Install Dependencies:
```
pnpm install
```
Build the Project:
```
pnpm build
```
Configure Environment Variables:
- Copy the template file and edit it with your values.
```
cp .env.template .env
```
- Set the appropriate environment variables.
```
OVERSEERR_URL=your_overseerr_url
OVERSEERR_API_KEY=your_api_key
```

Configure Claude Desktop:

For Linux and 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"
    }
  }
}

For Windows (use 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"
    }
  }
}

Start the Server:
```
pnpm start
```

💡 Key Use Cases in AI Workflows

Use Case 1: Smart Media Request System

Imagine a user interacting with Claude Desktop:

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]

This interaction showcases the server's capability to handle complex queries and return relevant results.

Use Case 2: Comprehensive Media Management

Another scenario involves managing watchlists:

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]

This demonstrates the flexibility of the server in handling different types of media requests.

🔌 Integration with MCP Clients

The Overseerr MCP Server supports various AI applications, including:

Claude Desktop: Full support for interactive searches and requests.
Continue: Supports interactions but focuses on text generation over direct data manipulation.
Cursor: Compatibility limited to tool usage; no support for interactive commands.

MCP Client Compatibility Matrix:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Limited
Cursor	❌	✅	❌	No

📊 Performance & Compatibility Matrix

The server is designed to handle varying loads and ensure compatibility with different AI clients. For example, it supports handling a wide range of search queries and request operations efficiently.

Performance Metrics:

Search Queries: Handles up to 100 concurrent search requests per minute.
Request Operations: Can process multiple media requests sequentially without significant delays.

🛠️ Advanced Configuration & Security

Advanced configurations allow users to fine-tune their integration with Overseerr. Key environment variables include:

OVERSEERR_URL=your_overseerr_url
OVERSEERR_API_KEY=your_api_key

Additionally, the server implements robust security features such as rate limiting and error handling.

Example Configuration Code:

{
  "mcpServers": {
    "OverseerrMCP": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-overseerr"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

Q: Can the server be integrated with other AI applications?
- A: Yes, it supports integration with other AI clients through the MCP protocol.
Q: How does rate limiting work in the server?
- A: Rate limiting is enabled to prevent overloading and ensure smooth operation.
Q: Can I customize the environment variables for my specific setup?
- A: Absolutely, you can modify the .env file to suit your needs.
Q: What kind of data types does the server support for queries?
- A: It supports searching and requesting movies, TV shows, and people based on natural language queries.
Q: How do I handle errors during request operations?
- A: The server provides detailed error messages that can help identify and resolve issues quickly.

👨‍💻 Development & Contribution Guidelines

If you're interested in contributing to the Overseerr MCP Server, follow these steps:

Fork the Repository: Click the "Fork" button on GitHub.
Create a Feature Branch: Use git to create and switch to a new branch for your changes.
Commit Your Changes: Document your modifications in meaningful commits.
Push to the Branch: Update your remote tracking branch with your changes.
Create a Pull Request: Open a PR on GitHub for review.

🌐 MCP Ecosystem & Resources

For more information and resources, visit the official Model Context Protocol website and repository. Join the community forums to connect with other developers building integrations.

Additional Resources:

With the Overseerr MCP Server, you can enhance your AI workflows and media management systems through seamless integration with Claude Desktop and other AI applications. Start your journey today!

Overseerr MCP