optical-context-mcp 0.1.1

FastMCP server for compressing large OCR-heavy PDFs into dense packed images for agent workflows.

Optical Context MCP

FastMCP server for compressing large, OCR-heavy PDFs into dense packed images for agent workflows.

Optical Context MCP is built for one specific problem: giving agents a practical way to work with large, visually structured PDFs without sending every page individually to a vision model.

It reads a local PDF, runs OCR with Mistral, recomposes the extracted text and figures into a much smaller set of packed images, and exposes those artifacts over MCP for batch retrieval.

What It Does

• reads a local PDF from the MCP host machine
• extracts page markdown and embedded images with Mistral OCR
• packs that content into dense PNGs that preserve visual grouping
• stores a manifest and job artifacts for follow-up retrieval
• lets an agent pull only the packed images it needs

Where It Fits

Use it for:

• operating manuals
• scanned handbooks
• product catalogs
• PDF slide decks
• visually structured OCR-heavy documents

Skip it for:

• tiny PDFs
• clean text-native PDFs where normal extraction is enough
• workflows that require exact page-faithful rendering
• cases where OCR cost is not justified

Example Result

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:

• source paper pages: 22
• previewed source page range: 15 to 21
• extracted images: 30
• packed output images: 6
• example packed image size: 986x1084
• example packed image file size: 536,697 bytes

This 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.

Install

python -m pip install "git+https://github.com/ChrBoebel/optical-context-mcp.git@v0.1.1"

Run directly from GitHub with uvx:

uvx --from git+https://github.com/ChrBoebel/optical-context-mcp@v0.1.1 optical-context-mcp

• MISTRAL_API_KEY is required for compress_pdf

Run

Default transport is stdio:

optical-context-mcp

Claude Code

Register the server in a project:

claude mcp add -s project optical-context -- uvx --from git+https://github.com/ChrBoebel/optical-context-mcp@v0.1.1 optical-context-mcp

Typical use:

1. call compress_pdf
2. inspect the returned manifest
3. fetch packed images with get_packed_images

MCP Tools

• compress_pdf: run OCR plus recomposition and create a stored job
• get_job_manifest: load metadata for an existing job
• get_packed_images: fetch one or more packed PNGs from an existing job

How It Works

flowchart LR
    A["Local PDF"] --> B["Mistral OCR"]
    B --> C["Page markdown + embedded images"]
    C --> D["Recomposition engine"]
    D --> E["Dense packed PNG images"]
    E --> F["Stored job artifacts"]
    F --> G["Agent fetches manifest or image batches over MCP"]

Why Packed Images Instead Of Just OCR Text

• section grouping
• table-like layout
• captions near figures
• visual adjacency between text and embedded graphics

For many vision-capable agents, that is a better intermediate format than a plain OCR dump.

Current Scope

• depends on Mistral OCR
• currently handles local file paths, not remote uploads
• optimized for compression and retrieval, not final polished markdown generation
• quality depends on OCR quality and the visual density of the source document

Roadmap

• make the OCR layer provider-agnostic so different OCR backends can be swapped behind the same MCP workflow

Development

uv venv --python /opt/homebrew/bin/python3.11 .venv
uv pip install --python .venv/bin/python -e ".[dev]"
.venv/bin/python -m pytest