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mcp-mistral-ocr
Model Context Protocol (MCP) Server for Mistral OCR API
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MCP Mistral OCR
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
- Build the Docker image:
docker build -t mcp-mistral-ocr .
- 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
- Install UV package manager:
pip install uv
- Create and activate virtual environment:
uv venv
source .venv/bin/activate # On Unix
# or
.venv\Scripts\activate # On Windows
- 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
orurl_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

user_AAM1qjrR
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!