| name | mistral-ocr |
| description | Extract text from images and PDFs using Mistral OCR API. Convert scanned documents to Markdown, JSON, or plain text. No external dependencies required. Use when you need OCR, extract text from images, convert PDFs to markdown, or digitize documents. |
| user-invocable | true |
| allowed-tools | Bash(curl:*), Read, Write |
| metadata | {"version":"2.1.4"} |
Mistral OCR
Extract text from images and PDFs using Mistral's dedicated OCR API. No external dependencies required.
Requirements
This skill requires a Mistral API key. If you don't have one, follow the guide in reference/getting-started.md.
API Key
The user must provide their Mistral API key. Ask for it if not available.
Option 1 (Recommended for AI agents): User provides key directly in message:
"Use this Mistral key: aBc123XyZ..."
"Convert this PDF to markdown, my API key is aBc123XyZ..."
Option 2: Environment variable $MISTRAL_API_KEY
Option 3: Claude Code settings (~/.claude/settings.json)
If no key is available, guide the user to get one at console.mistral.ai.
API Endpoint
Use the dedicated OCR endpoint for all document processing:
POST https://api.mistral.ai/v1/ocr
Model: mistral-ocr-latest
Features
1. PDF → Markdown (Direct, no conversion needed!)
curl -s "https://api.mistral.ai/v1/ocr" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-ocr-latest",
"document": {
"type": "document_url",
"document_url": "https://example.com/document.pdf"
}
}'
2. Image → Text
Works with JPG, PNG, WEBP, GIF:
curl -s "https://api.mistral.ai/v1/ocr" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-ocr-latest",
"document": {
"type": "image_url",
"image_url": "https://example.com/image.jpg"
}
}'
3. Local Files (Base64 Data URL)
For local PDFs or images, encode as base64 and use a data URL.
ALWAYS use curl (works on all platforms including Windows via Git Bash):
BASE64=$(base64 -w0 document.pdf)
curl -s "https://api.mistral.ai/v1/ocr" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-ocr-latest",
"document": {
"type": "document_url",
"document_url": "data:application/pdf;base64,'"$BASE64"'"
}
}'
BASE64=$(base64 -w0 image.png)
curl -s "https://api.mistral.ai/v1/ocr" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-ocr-latest",
"document": {
"type": "image_url",
"image_url": "data:image/png;base64,'"$BASE64"'"
}
}'
MIME types:
- PDF:
data:application/pdf;base64,...
- PNG:
data:image/png;base64,...
- JPG:
data:image/jpeg;base64,...
- WEBP:
data:image/webp;base64,...
4. Structured JSON Output
For invoices, forms, tables - ask for JSON in a follow-up or use Document AI annotations.
Response Format
The API returns markdown directly:
{
"pages": [
{
"index": 0,
"markdown": "# Document Title\n\nExtracted content here...",
"images": [],
"tables": [],
"dimensions": {"dpi": 200, "height": 842, "width": 595}
}
],
"model": "mistral-ocr-latest",
"usage_info": {"pages_processed": 1, "doc_size_bytes": 12345}
}
Workflow
User requests OCR from image or PDF
- Get API key - Ask user if not in environment
- Determine input type (URL or local file)
- For local files, ALWAYS use temp file approach (avoids "Argument list too long" error):
TMPDIR="${TMPDIR:-${TEMP:-/tmp}}"
base64 -w0 "document.pdf" > "$TMPDIR/b64.txt"
echo '{"model":"mistral-ocr-latest","document":{"type":"document_url","document_url":"data:application/pdf;base64,'$(cat "$TMPDIR/b64.txt")'"}}' > "$TMPDIR/request.json"
curl -s "https://api.mistral.ai/v1/ocr" \
-H "Authorization: Bearer YOUR_API_KEY_HERE" \
-H "Content-Type: application/json" \
-d @"$TMPDIR/request.json" > "$TMPDIR/response.json"
node -e "const fs=require('fs'); const r=JSON.parse(fs.readFileSync('$TMPDIR/response.json')); console.log(r.pages.map(p=>p.markdown).join('\n\n---\n\n'))"
- Save to .md file using Write tool
- Confirm file location to user
IMPORTANT: Cross-Platform Compatibility
- ALWAYS use curl (works on Windows via Git Bash)
- ALWAYS use
-d @file for request body (handles large files)
- NEVER use jq - use node instead to parse JSON
- Use
${TMPDIR:-${TEMP:-/tmp}} for temp files (works on all systems)
- Copy response.json to user directory before parsing with node on Windows
Usage Examples
When the user says:
| User Request | Action |
|---|
| "Convert this PDF to markdown" | OCR the PDF, save as .md file |
| "Extract text from this image" | OCR the image, return text |
| "Give me a .md of this document" | OCR and save as .md file |
| "What does this PDF say?" | OCR and summarize content |
| "OCR this receipt" | Extract text, optionally structure as JSON |
Error Handling
| Error | Cause | Solution |
|---|
| 401 Unauthorized | Invalid API key | Verify key, guide to getting-started.md |
| 400 Bad Request | Invalid document | Check format and URL accessibility |
| 3310 File fetch error | URL not accessible | Use base64 for local files |
| Rate limit | Too many requests | Wait and retry |
Supported Formats
| Format | Support |
|---|
| PDF | ✅ Direct (no conversion) |
| PNG | ✅ Direct |
| JPG/JPEG | ✅ Direct |
| WEBP | ✅ Direct |
| GIF | ✅ Direct |
No external dependencies required! Unlike other OCR solutions, Mistral OCR handles PDFs directly without needing pdftoppm, ImageMagick, or any other tools.
Pricing
As of 2025, Mistral OCR pricing:
- $2 per 1,000 pages
- 50% discount with Batch API
Check current rates at mistral.ai/pricing
References
Skill by Parlamento AI