visual-annotation-mcp 0.1.0

MCP server: navigate, inspect elements, screenshot, and annotate web pages with smart color contrast and background blur.

Visual Annotation MCP

A Model Context Protocol server that lets an LLM open web pages, list the interactive elements on them, take screenshots, and draw annotations — circles, ellipses, rectangles, arrows, and labeled text boxes — with smart color contrast and optional background blur.

Built on top of Playwright (headless Chromium) and Pillow.

What it does

Given a URL, the server exposes a small set of tools that together form a simple workflow:

navigate  →  inspect_elements  →  highlight_element  →  (annotate_last_image)

1. navigate — open a URL in a headless Chromium.
2. inspect_elements — return JSON for every visible link, button, and similar control with a stable id (e0, e1, ...), its text/aria label, href, and CSS bounding box.
3. highlight_element — screenshot a region around an element by id and draw an annotation on it. The crop is automatically expanded to include a few nearby interactive elements so the viewer has visual context.
4. screenshot_viewport / screenshot_element — raw screenshots with no annotation.
5. annotate_last_image — draw additional shapes/labels on the image produced by the previous tool call, so annotations can be stacked.

All shape drawing, color resolution, and image effects live in visual_annotation_mcp/annotate.py and can be used as a normal Python library independent of the MCP server.

Features

Shapes

• circle / ellipse — an aspect-ratio ellipse sized to fully enclose the element's bounding box (no more "red dot in the middle of a wide link").
• rectangle — bounding rectangle with a small margin.
• arrow — line with a filled arrowhead, pointing at the element from whichever nearby edge (top / bottom / right / left) actually fits in the cropped image.
• text — standalone text box; also available as a label parameter on any of the above shapes.

Dynamic crop context

The crop around the target is grown to include the N nearest interactive elements (by bounding-box edge distance) so the viewer can orient themselves. Oversized container links (>50% of the viewport in either dimension) are excluded so one giant hero-banner link can't bloat the crop. min_context is tunable per call:

min_context	Use case
0	Tight crop, element only
3-5	Compact reference
6-10	Default (6) — generous reference
12-20	Wide overview

Smart color resolution

color="auto" (the default) uses a preferred-with-fallback model:

• The annotation color defaults to prefer_color="red".
• Before drawing, the surrounding pixels are sampled. If the Euclidean RGB distance from the average surround to the preferred color is at least min_contrast (default 140), the preferred color is used.
• Otherwise the system falls back to the palette color (red, lime, blue, yellow, magenta, cyan) with the greatest distance from that average — e.g. on target.com's red header strip, preferred red falls back to cyan.
• Passing an explicit color (color="#00ff00", color="lime", etc.) bypasses the fallback entirely.
• prefer_color accepts any CSS color string, so users can say "always circle in lime" while still getting automatic contrast correction.

Labels

label="Click here" draws a bordered text box near the element. The fill and text colors are picked automatically for legibility against whatever pixels sit under the label (light-on-dark or dark-on-light), and the border matches the shape's resolved color. label_position can be auto (bottom → top → right → left), or pinned to any side.

Background blur

blur_background=True applies a feathered Gaussian blur to everything outside the target's bounding box, leaving the element sharp. Good for "which button is X" style screenshots where the rest of the page is noisy.

Installation

From PyPI (recommended)

pip install visual-annotation-mcp
visual-annotation-mcp-install-browsers   # downloads headless Chromium

The second step is required once per machine: pip installs the Python package but not the browser binary Playwright needs. It's equivalent to python -m playwright install chromium but discoverable from the installed console scripts.

Python 3.11+ is required.

From source (for development)

git clone https://github.com/mstocker1/Visual_Annotation_MCP
cd Visual_Annotation_MCP

python -m venv .venv
# Windows
.venv\Scripts\pip install -e .
.venv\Scripts\visual-annotation-mcp-install-browsers
# macOS / Linux
source .venv/bin/activate
pip install -e .
visual-annotation-mcp-install-browsers

Wiring it into Claude Code

The repo ships with a project-scoped .mcp.json:

{
  "mcpServers": {
    "visual-annotation": {
      "type": "stdio",
      "command": "${VISUAL_ANNOTATION_PYTHON:-python}",
      "args": ["-m", "visual_annotation_mcp"],
      "env": {}
    }
  }
}

Open this repo in Claude Code and approve the project MCP server when prompted. Run /mcp to confirm visual-annotation is connected.

If python isn't on the GUI PATH (common on Windows)

Any of these work:

1. Set VISUAL_ANNOTATION_PYTHON to the venv interpreter before starting Claude Code (e.g. C:\path\to\repo\.venv\Scripts\python.exe).
2. Replace command in .mcp.json with that full path directly.
3. On Windows, point command/args at powershell + scripts\run_visual_mcp.ps1. On macOS/Linux use scripts/run_visual_mcp.sh.

The bundled helper scripts prefer the repo's .venv automatically.

First launch can be slow

The first call that needs a browser downloads and launches Chromium. If Claude Code times out the MCP handshake, start it with a higher startup timeout, e.g.

MCP_TIMEOUT=120000 claude

Running without Claude

The package also runs as a standalone MCP stdio server:

python -m visual_annotation_mcp

For end-to-end verification without any MCP client at all, run the smoke test:

python tests/smoke_test.py

It exercises every shape, the label, the blur effect, the contrast fallback, annotate_last_image stacking, and raw screenshots against an in-memory data URL. It exits non-zero on any assertion failure.

Tool reference

navigate(url, wait_until="load")

Go to a URL (HTTP/S only). Clears element ids from any previous inspect_elements.

inspect_elements()

Return JSON with every visible link, button, role="button", role="link", role="menuitem", and submit/reset/button input on the current page. Each entry has id, tag, text, aria_label, role, href, and box_css (x, y, width, height in CSS pixels).

screenshot_viewport(full_page=False)

Capture the current viewport (or the full scrolling page when full_page=True) as a PNG.

screenshot_element(element_id)

Tight PNG screenshot of a single DOM element by id.

highlight_element(element_id, ...)

Screenshot a region around an element and draw an annotation on it.

Parameter	Default	Description
element_id	—	Id from a previous inspect_elements call.
padding	16	Extra pixel margin around the computed crop.
style	"circle"	circle, ellipse, rectangle, arrow, or text.
min_context	6	Include at least this many nearby interactive elements in the crop.
color	"auto"	"auto" for preferred-with-fallback, or any CSS color to force.
prefer_color	"red"	Preferred color for auto mode; any CSS color.
min_contrast	140	RGB distance threshold before the auto picker falls back to the palette.
label	None	Optional text drawn in a bordered text box near the element.
label_position	"auto"	auto / top / bottom / left / right.
blur_background	False	Gaussian-blur everything outside the element's bbox.
stroke_width	4	Shape outline width in pixels.

annotate_last_image(x, y, width, height, ...)

Draw an additional annotation on the image from the previous call, at the given pixel bounding box. Accepts all of the visual parameters above (style, color, prefer_color, min_contrast, label, label_position, blur_background, stroke_width). Calls stack, so you can build up a multi-shape diagram with repeated invocations.

Optional URL allowlist

Set VISUAL_ANNOTATION_ALLOWED_HOSTS in the MCP server's environment to restrict which hosts can be navigated to:

{
  "mcpServers": {
    "visual-annotation": {
      "type": "stdio",
      "command": "${VISUAL_ANNOTATION_PYTHON:-python}",
      "args": ["-m", "visual_annotation_mcp"],
      "env": {
        "VISUAL_ANNOTATION_ALLOWED_HOSTS": "example.com,docs.example.com"
      }
    }
  }
}

Comma-separated hostnames, no scheme. Any navigation to a host outside the list raises an error. Leave unset to allow all hosts.

Layout

Visual_Annotation_MCP/
├── .github/workflows/
│   └── publish.yml           # Build + PyPI trusted publishing on git tag
├── .mcp.json                 # Project-scoped MCP config
├── CLAUDE.md                 # Claude Code project instructions
├── LICENSE                   # MIT
├── pyproject.toml            # PEP 621 metadata, hatch-vcs dynamic versioning
├── README.md
├── scripts/
│   ├── run_visual_mcp.ps1    # Windows launcher (prefers repo .venv)
│   └── run_visual_mcp.sh     # POSIX launcher (prefers repo .venv)
├── tests/
│   └── smoke_test.py         # End-to-end check, no MCP stdio needed
└── visual_annotation_mcp/
    ├── __main__.py           # python -m visual_annotation_mcp
    ├── annotate.py           # Shape primitives, blur, contrast picker
    ├── browser_session.py    # Playwright session wrapper
    ├── install.py            # visual-annotation-mcp-install-browsers
    ├── security.py           # Optional host allowlist
    └── server.py             # FastMCP tool definitions

Releasing

Versioning is driven by git tags via hatch-vcs: the version injected into the built wheel is whatever the most recent v* tag is, with a dev suffix for untagged commits. You never edit a version number by hand.

One-time PyPI setup (per project):

1. Create the project once on PyPI (either by uploading the very first release manually with twine, or by configuring a pending trusted publisher without any release yet).
2. In the project's Publishing settings on pypi.org, add a new Trusted Publisher with:
   • PyPI project name: visual-annotation-mcp
   • Owner: mstocker1
   • Repository: Visual_Annotation_MCP
   • Workflow name: publish.yml
   • Environment name: pypi
3. On GitHub, create an environment named pypi under Settings → Environments (no secrets needed with trusted publishing; optionally add a deployment protection rule for extra safety).

After that, cutting a release is:

git tag v0.1.0
git push origin v0.1.0

.github/workflows/publish.yml will build the sdist and wheel, upload them to PyPI, and attach the artefacts to the workflow run.

Regular pushes and pull requests also run the build step (without publishing) so broken packaging changes get caught in CI.

License

MIT — see LICENSE. This project depends on third-party libraries (mcp, playwright, pillow) via pip and does not embed their source code, so their licenses do not propagate to your use of this package.