# Mcp Image Recognition

> An MCP server that provides image recognition 👀 capabilities using Anthropic and OpenAI vision APIs

- **Type:** MCP server
- **Install:** `agentstack add mcp-mario-andreschak-mcp-image-recognition`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [mario-andreschak](https://agentstack.voostack.com/s/mario-andreschak)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [mario-andreschak](https://github.com/mario-andreschak)
- **Source:** https://github.com/mario-andreschak/mcp-image-recognition

## Install

```sh
agentstack add mcp-mario-andreschak-mcp-image-recognition
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# MCP Image Recognition Server

An MCP server that provides image recognition capabilities using Anthropic and OpenAI vision APIs. Version 0.1.2.

## Features

- Image description using Anthropic Claude Vision or OpenAI GPT-4 Vision
- Support for multiple image formats (JPEG, PNG, GIF, WebP)
- Configurable primary and fallback providers
- Base64 and file-based image input support
- Optional text extraction using Tesseract OCR

## Requirements

- Python 3.8 or higher
- Tesseract OCR (optional) - Required for text extraction feature
  - Windows: Download and install from [UB-Mannheim/tesseract](https://github.com/UB-Mannheim/tesseract/wiki)
  - Linux: `sudo apt-get install tesseract-ocr`
  - macOS: `brew install tesseract`

## Installation

1. Clone the repository:
```bash
git clone https://github.com/mario-andreschak/mcp-image-recognition.git
cd mcp-image-recognition
```

2. Create and configure your environment file:
```bash
cp .env.example .env
# Edit .env with your API keys and preferences
```

3. Build the project:
```bash
build.bat
```

## Usage

### Running the Server
Spawn the server using python:
```bash
python -m image_recognition_server.server
```

Start the server using batch instead:
```bash
run.bat server
```

Start the server in development mode with the MCP Inspector:
```bash
run.bat debug
```

### Available Tools

1. `describe_image`
   - Input: Base64-encoded image data and MIME type
   - Output: Detailed description of the image

2. `describe_image_from_file`
   - Input: Path to an image file
   - Output: Detailed description of the image

### Environment Configuration

- `ANTHROPIC_API_KEY`: Your Anthropic API key.
- `OPENAI_API_KEY`: Your OpenAI API key.
- `VISION_PROVIDER`: Primary vision provider (`anthropic` or `openai`).
- `FALLBACK_PROVIDER`: Optional fallback provider.
- `LOG_LEVEL`: Logging level (DEBUG, INFO, WARNING, ERROR).
- `ENABLE_OCR`: Enable Tesseract OCR text extraction (`true` or `false`).
- `TESSERACT_CMD`: Optional custom path to Tesseract executable.
- `OPENAI_MODEL`: OpenAI Model (default: `gpt-4o-mini`). Can use OpenRouter format for other models (e.g., `anthropic/claude-3.5-sonnet:beta`).
- `OPENAI_BASE_URL`: Optional custom base URL for the OpenAI API.  Set to `https://openrouter.ai/api/v1` for OpenRouter.
- `OPENAI_TIMEOUT`: Optional custom timeout (in seconds) for the OpenAI API.

### Using OpenRouter

OpenRouter allows you to access various models using the OpenAI API format. To use OpenRouter, follow these steps:

1.  Obtain an OpenAI API key from OpenRouter.
2.  Set `OPENAI_API_KEY` in your `.env` file to your OpenRouter API key.
3.  Set `OPENAI_BASE_URL` to `https://openrouter.ai/api/v1`.
4.  Set `OPENAI_MODEL` to the desired model using the OpenRouter format (e.g., `anthropic/claude-3.5-sonnet:beta`).
5. Set `VISION_PROVIDER` to `openai`.

### Default Models

- Anthropic: `claude-3.5-sonnet-beta`
- OpenAI: `gpt-4o-mini`
- OpenRouter: Use the `anthropic/claude-3.5-sonnet:beta` format in `OPENAI_MODEL`.

## Development

### Running Tests

Run all tests:
```bash
run.bat test
```

Run specific test suite:
```bash
run.bat test server
run.bat test anthropic
run.bat test openai
```

### Docker Support

Build the Docker image:
```bash
docker build -t mcp-image-recognition .
```

Run the container:
```bash
docker run -it --env-file .env mcp-image-recognition
```

## License

MIT License - see LICENSE file for details.

## Release History

- **0.1.2** (2025-02-20): Improved OCR error handling and added comprehensive test coverage for OCR functionality
- **0.1.1** (2025-02-19): Added Tesseract OCR support for text extraction from images (optional feature)
- **0.1.0** (2025-02-19): Initial release with Anthropic and OpenAI vision support

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [mario-andreschak](https://github.com/mario-andreschak)
- **Source:** [mario-andreschak/mcp-image-recognition](https://github.com/mario-andreschak/mcp-image-recognition)
- **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:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **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-mario-andreschak-mcp-image-recognition
- Seller: https://agentstack.voostack.com/s/mario-andreschak
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.