Optical Context MCP

Name: Optical Context MCP
Author: ChrBoebel

by ChrBoebelversion 0.1.5i★ –iRuns on your computer

Registry nameio.github.ChrBoebel/optical-context-mcp

Install

Overview

Description from the maker (original English)

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "optical-context": {
      "command": "uvx",
      "args": [
        "optical-context-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add optical-context -- uvx optical-context-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "optical-context": {
      "command": "uvx",
      "args": [
        "optical-context-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "optical-context": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "optical-context-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account ChrBoebel, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2754 servers in this category
Published to the registry more recently than 41% of the other 2754 servers in this category
Basic checks equal to or better than 100% of the other 2754 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Optical Context MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.5) reached the registry 39 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Optical Context MCP

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.

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
optionally sizes embedded figures with a bundled technical-document model
stores a manifest and temp 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 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_pdf
packed images are always stored locally under the system temp directory
compress_pdf returns up to 30 packed images inline by default
the adaptive sizing checkpoint is bundled with the package
adaptive sizing activates automatically when torch and torchvision are available
set OPTICAL_CONTEXT_DISABLE_ADAPTIVE_SIZING=1 to force the legacy fixed sizing
set OPTICAL_CONTEXT_ADAPTIVE_MODEL_PATH=/path/to/model.pt to override the bundled checkpoint

For pinned shared setups:

uvx --from optical-context-mcp==0.1.5 optical-context-mcp

Run

Default transport is stdio:

optical-context-mcp

Claude Code

claude mcp add -s project optical-context -- uvx optical-context-mcp

Typical use:

call compress_pdf
inspect the returned manifest
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
stores artifacts in the local system temp directory by default
optimized for compression and retrieval, not final polished markdown generation
quality depends on OCR quality and the visual density of the source document
adaptive sizing falls back safely to fixed medium image sizing when the ML runtime is absent

Roadmap

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

Feedback

Tried Optical Context in a document workflow? You can voluntarily share what worked and what got in the way using the feedback form. Include your package version, MCP client and a brief description of the document type. Do not attach private PDFs or API keys. Feedback is public on GitHub.

See the changelog for changes between published versions.

Development

uv venv --python /opt/homebrew/bin/python3.11 .venv

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

Publishing

Keep pyproject.toml and both versions in server.json synchronized. Run the Publish PyPI workflow on the reviewed release commit. It checks the wheel, runs tests against the installed distribution, publishes to PyPI, then publishes the same version to the MCP Registry. Both use GitHub OIDC; no local publishing token is required. If only the registry job fails, rerun that failed job rather than republishing the immutable PyPI version. When a workflow correction requires a new run, select registry_only to retry registry publication without uploading the existing PyPI version again.

Create the matching GitHub release from that same commit and attach the workflow's python-distributions artifact.

Optical Context MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

What It Does

Where It Fits

Example Result

Install

Run

Claude Code

MCP Tools

How It Works

Why Packed Images Instead Of Just OCR Text

Current Scope

Roadmap

Feedback

Development

Publishing