io.github.habedi/omni-lpr MCP Server

<div align="center"> <picture> <img alt="Omni-LPR Logo" src="logo.svg" width="300"> </picture> <br> <h2>Omni-LPR</h2>

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A multi-interface (REST and MCP) server for automatic license plate recognition

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Omni-LPR is a self-hostable server that provides automatic license plate recognition (ALPR) capabilities via a REST API and the Model Context Protocol (MCP). It can be used both as a standalone ALPR microservice and as an ALPR toolbox for AI agents and large language models (LLMs).

Why Omni-LPR?

Using Omni-LPR can have the following benefits:

• Decoupling. Your main application can be in any programming language. It doesn't need to be tangled up with Python or specific ML dependencies because the server handles all of that.

• Multiple Interfaces. You aren't locked into one way of communicating. You can use a standard REST API from any app, or you can use MCP, which is designed for AI agent integration.

• Ready-to-Deploy. You don't have to build it from scratch. There are pre-built Docker images that are easy to deploy and start using immediately.

• Hardware Acceleration. The server is optimized for the hardware you have. It supports generic CPUs (ONNX), Intel CPUs (OpenVINO), and NVIDIA GPUs (CUDA).

• Asynchronous I/O. It's built on Starlette, which means it has high-performance, non-blocking I/O. It can handle many concurrent requests without getting bogged down.

• Scalability. Because it's a separate service, it can be scaled independently of your main application. If you suddenly need more ALPR power, you can scale Omni-LPR up without touching anything else.

See the ROADMAP.md for the list of implemented and planned features.

[!IMPORTANT] Omni-LPR is in early development, so bugs and breaking API changes are expected. Please use the issues page to report bugs or request features.

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Quickstart

You can get started with Omni-LPR in a few minutes by following the steps described below.

1. Install the Server

You can install Omni-LPR using pip:

pip install omni-lpr

2. Start the Server

When installed, start the server with a single command:

omni-lpr

By default, the server will be listening on http://127.0.0.1:8000. You can confirm it's running by accessing the health check endpoint:

curl http://127.0.0.1:8000/api/health
# Sample expected output: {"status": "ok", "version": "0.3.4"}

3. Recognize a License Plate

Now you can make a request to recognize a license plate from an image. The example below uses a publicly available image URL.

curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"path": "https://www.olavsplates.com/foto_n/n_cx11111.jpg"}' \
  http://127.0.0.1:8000/api/v1/tools/detect_and_recognize_plate_from_path/invoke

You should receive a JSON response with the detected license plate information.

Usage

Omni-LPR exposes its capabilities as "tools" that can be called via a REST API or over the MCP.

Available Tools

The server provides tools for listing models, recognizing plates from image data, and recognizing plates from a path.

• list_models: Lists the available detector and OCR models.

• Tools that process image data (provided as Base64 or file upload):

  • recognize_plate: Recognizes text from a pre-cropped license plate image.
  • detect_and_recognize_plate: Detects and recognizes all license plates in a full image.

• Tools that process an image path (a URL or local file path):

  • recognize_plate_from_path: Recognizes text from a pre-cropped license plate image at a given path.
  • detect_and_recognize_plate_from_path: Detects and recognizes plates in a full image at a given path.

For more details on how to use the different tools and provide image data, please see the API Documentation.

REST API

The REST API provides a standard way to interact with the server. All tool endpoints are available under the /api/v1 prefix. Once the server is running, you can access interactive API documentation in the Swagger UI at http://127.0.0.1:8000/api/v1/apidoc/swagger.

MCP Interface

The server also exposes its tools over the MCP for integration with AI agents and LLMs. The MCP endpoint is available at http://127.0.0.1:8000/mcp/, via streamable HTTP.

You can use a tool like MCP Inspector to explore the available MCP tools.

<div align="center"> <picture> <img src="docs/assets/screenshots/mcp-inspector-3.png" alt="MCP Inspector Screenshot" width="auto"> </picture> </div>

Integration

You can connect any client that supports the MCP protocol to the server. The following examples show how to use the server with LM Studio.

LM Studio Configuration

{
    "mcpServers": {
        "omni-lpr-local": {
            "url": "http://127.0.0.1:8000/mcp/"
        }
    }
}

Tool Usage Examples

The screenshot of using the list_models tool in LM Studio to list the available models for the APLR.

<div align="center"> <picture> <img src="docs/assets/screenshots/lmstudio-list-models-1.png" alt="LM Studio Screenshot 1" width="auto" height="auto"> </picture> </div>

The screenshot below shows using the detect_and_recognize_plate_from_path tool in LM Studio to detect and recognize the license plate from an image available on the web.

<div align="center"> <picture> <img src="docs/assets/screenshots/lmstudio-detect-plates-1.png" alt="LM Studio Screenshot 2" width="auto" height="auto"> </picture> </div>

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Documentation

Omni-LPR documentation is available here.

Examples

Check out the examples directory for usage examples.

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Contributing

Contributions are always welcome! Please see CONTRIBUTING.md for details on how to get started.

License

Omni-LPR is licensed under the MIT License (see LICENSE).

Acknowledgements

• This project uses the awesome fast-plate-ocr and fast-alpr Python libraries.
• The project logo is from SVG Repo.

<!-- Need to add this line for MCP registry publication --> <!-- mcp-name: io.github.habedi/omni-lpr -->