Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.
{
"mcpServers": {
"io-github-chrboebel-optical-context-mcp": {
"args": [
"optical-context-mcp"
],
"command": "uvx"
}
}
}Are you the author?
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Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.
Is it safe?
No known CVEs for optical-context-mcp.
No authentication — any process on your machine can connect.
License not specified.
Is it maintained?
Last commit 4 days ago. 1 stars.
Will it work with my client?
Transport: stdio, sse, http. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'optical-context-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
No known vulnerabilities.
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Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.
Optical Context MCP is built for one specific job: turning large, visually structured PDFs into a smaller set of retrievable packed images for agent workflows.
It reads a local PDF, runs OCR with Mistral, recomposes the extracted text and figures into dense PNGs, and exposes those artifacts over MCP for batch retrieval.
Use it for:
Skip it for:
The image below shows a real local validation run on a public research paper with dense text, figures, charts, and page-level visual structure. The packed image on the right consolidates the seven source pages shown on the left.
Example local run facts from the generated manifest:
986x1084536,697 bytesThis example shows the intended workflow: take a long, visually structured PDF and compress it into a smaller set of retrievable packed images that still preserve the visual structure of the source.
python -m pip install optical-context-mcp
Install with the adaptive sizing runtime:
python -m pip install "optical-context-mcp[ml]"
Run without installing:
uvx optical-context-mcp
MISTRAL_API_KEY is required for compress_pdfcompress_pdf returns up to 30 packed images inline by defaulttorch and torchvision are availableOPTICAL_CONTEXT_DISABLE_ADAPTIVE_SIZING=1 to force the legacy fixed sizingOPTICAL_CONTEXT_ADAPTIVE_MODEL_PATH=/path/to/model.pt to override the bundled checkpointFor pinned shared setups:
uvx --from optical-context-mcp==0.1.4 optical-context-mcp
Default transport is stdio:
optical-context-mcp
Register the server in a project:
claude mcp
... [View full README on GitHub](https://github.com/ChrBoebel/optical-context-mcp#readme)