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2025-04-09

Servidor de protocolo de contexto del modelo (MCP) para la API de OCR de Mistral

3 years

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MCP Mistral OCR

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An MCP server that provides OCR capabilities using Mistral AI's OCR API. This server can process both local files and URLs, supporting images and PDFs.

Features

  • Process local files (images and PDFs) using Mistral's OCR
  • Process files from URLs with explicit file type specification
  • Support for multiple file formats (JPG, PNG, PDF, etc.)
  • Results saved as JSON files with timestamps
  • Docker containerization
  • UV package management

Environment Variables

  • MISTRAL_API_KEY: Your Mistral AI API key
  • OCR_DIR: Directory path for local file processing. Inside the container, this is always mapped to /data/ocr

Installation

Installing via Smithery

To install Mistral OCR for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @everaldo/mcp/mistral-crosswalk --client claude

Using Docker

  1. Build the Docker image:
docker build -t mcp-mistral-ocr .
  1. Run the container:
docker run -e MISTRAL_API_KEY=your_api_key -e OCR_DIR=/data/ocr -v /path/to/local/files:/data/ocr mcp-mistral-ocr

Local Development

  1. Install UV package manager:
pip install uv
  1. Create and activate virtual environment:
uv venv
source .venv/bin/activate  # On Unix
# or
.venv\Scripts\activate  # On Windows
  1. Install dependencies:
uv pip install .

Claude Desktop Configuration

Add this configuration to your claude_desktop_config.json:

{
  "mcpServers": {
    "mistral-ocr": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "MISTRAL_API_KEY",
        "-e",
        "OCR_DIR",
        "-v",
        "C:/path/to/your/files:/data/ocr",
        "mcp-mistral-ocr:latest"
      ],
      "env": {
        "MISTRAL_API_KEY": "<YOUR_MISTRAL_API_KEY>",
        "OCR_DIR": "C:/path/to/your/files"
      }
    }
  }
}

Available Tools

1. process_local_file

Process a file from the configured OCR_DIR directory.

{
    "name": "process_local_file",
    "arguments": {
        "filename": "document.pdf"
    }
}

2. process_url_file

Process a file from a URL. Requires explicit file type specification.

{
    "name": "process_url_file",
    "arguments": {
        "url": "https://example.com/document",
        "file_type": "image"  // or "pdf"
    }
}

Output

OCR results are saved in JSON format in the output directory inside OCR_DIR. Each result file is named using the following format:

  • For local files: {original_filename}_{timestamp}.json
  • For URLs: {url_filename}_{timestamp}.json or url_document_{timestamp}.json if no filename is found in the URL

The timestamp format is YYYYMMDD_HHMMSS.

Supported File Types

  • Images: JPG, JPEG, PNG, GIF, WebP
  • Documents: PDF and other document formats supported by Mistral OCR

Limitations

  • Maximum file size: 50MB (enforced by Mistral API)
  • Maximum document pages: 1000 (enforced by Mistral API)

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    Reviews

    2 (1)
    Avatar
    user_AAM1qjrR
    2025-04-16

    I've been using mcp-mistral-ocr developed by everaldo, and it's a game-changer for my OCR needs! The tool is incredibly efficient and accurate in converting images and scanned documents into editable text. Its seamless integration and user-friendly interface make it stand out. Highly recommend checking it out at https://github.com/everaldo/mcp-mistral-ocr!