# Omni Lpr > A multi-interface (REST and MCP) server for automatic license plate recognition 🚗 - **Type:** MCP server - **Install:** `agentstack add mcp-habedi-omni-lpr` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [habedi](https://agentstack.voostack.com/s/habedi) - **Installs:** 0 - **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [habedi](https://github.com/habedi) - **Source:** https://github.com/habedi/omni-lpr ## Install ```sh agentstack add mcp-habedi-omni-lpr ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About Omni-LPR [](https://github.com/habedi/omni-lpr/actions/workflows/tests.yml) [](https://codecov.io/gh/habedi/omni-lpr) [](https://www.codefactor.io/repository/github/habedi/omni-lpr) [](https://github.com/habedi/omni-lpr) [](https://pypi.org/project/omni-lpr/) [](https://github.com/habedi/omni-lpr/blob/main/LICENSE) [](https://github.com/habedi/omni-lpr/tree/main/docs) [](https://github.com/habedi/omni-lpr/tree/main/examples) [](https://github.com/habedi/omni-lpr/pkgs/container/omni-lpr-cpu) [](https://github.com/habedi/omni-lpr/pkgs/container/omni-lpr-openvino) [](https://github.com/habedi/omni-lpr/pkgs/container/omni-lpr-cuda) A multi-interface (REST and MCP) server for automatic license plate recognition --- 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](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](https://github.com/habedi/omni-lpr/issues) to report bugs or request features. --- ### 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`: ```sh pip install omni-lpr ``` #### 2. Start the Server When installed, start the server with a single command: ```sh 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: ```sh 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. ```sh 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](docs/README.md). #### 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](https://github.com/modelcontextprotocol/inspector) to explore the available MCP tools. ### 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](https://lmstudio.ai/). #### LM Studio Configuration ```json { "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. 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](https://www.olavsplates.com/foto_n/n_cx11111.jpg). --- ### Documentation Omni-LPR documentation is available [here](docs). #### Examples Check out the [examples](examples) directory for usage examples. --- ### Contributing Contributions are always welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details on how to get started. ### License Omni-LPR is licensed under the MIT License (see [LICENSE](LICENSE)). ### Acknowledgements - This project uses the awesome [fast-plate-ocr](https://github.com/ankandrew/fast-plate-ocr) and [fast-alpr](https://github.com/ankandrew/fast-alpr) Python libraries. - The project logo is from [SVG Repo](https://www.svgrepo.com/svg/237124/license-plate-number). ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [habedi](https://github.com/habedi) - **Source:** [habedi/omni-lpr](https://github.com/habedi/omni-lpr) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-habedi-omni-lpr - Seller: https://agentstack.voostack.com/s/habedi - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.