
Optical Context MCP
This server turns long, visually dense PDFs into compressed image sets that preserve layout and structure. It wraps Mistral OCR behind three MCP tools: compress_pdf reads a local file and runs extraction plus recomposition, get_job_manifest returns metadata about the packed output, and get_packed_images fetches the resulting PNGs in batches. It bundles an adaptive sizing model that attempts to estimate figure dimensions intelligently, falling back to fixed sizing when the ML runtime is absent. Reach for it when you're working with operating manuals, scanned handbooks, or technical documents where visual grouping matters more than clean text extraction. Artifacts live in the system temp directory and jobs are identified by UUID for follow-up retrieval.
- GitHub stars
- 1
- tools
- 3
- transport
- STDIO
- last updated
- Sep 29, 2026
Tools
3 toolsPublic tool metadata for what this MCP can expose to an agent.
compress_pdfRun OCR plus recomposition and create a stored job.
Run OCR plus recomposition and create a stored job.
No parameter schema in public metadata yet.
get_job_manifestLoad metadata for an existing job.
Load metadata for an existing job.
No parameter schema in public metadata yet.
get_packed_imagesFetch one or more packed PNGs from an existing job.
Fetch one or more packed PNGs from an existing job.
No parameter schema in public metadata yet.
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Optical Context MCP
Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.
Details
- Repository
- github.com/chrboebel/optical-context-mcp
- Website
- github.com/ChrBoebel/optical-context-mcp
- Package
optical-context-mcp- Transport
- STDIO
- Registry
- MCP registry, active