Snapgrab

io.github.ArkNillPublished by GitHub account ArkNillv0.1.0Updated Oct 24, 2026

URL to screenshot with metadata. Python MCP server. Claude Vision optimized.

VerifiedSTDIODesktop onlyWeb Search & Scraping Browser Automation Media & Design

Server details

Identifier: io.github.ArkNill/snapgrab
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Sep 16, 2026

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Overview

AI-generated overview

Snapgrab lets an assistant capture screenshots of web pages with page metadata, viewport presets, and Claude Vision token estimates.

What you can do: Snapgrab is a local Python MCP server that renders a URL with Playwright and returns a screenshot plus page metadata. Its tools include capture_screenshot (image with metadata and a vision token estimate), capture_comparison (compare desktop versus mobile or other viewports), and extract_page_metadata (metadata only, no image). It supports PNG, JPEG, and PDF output, full-page capture, CSS selector element capture, dark mode, and auto-resizing to 1568px for Claude Vision.
When to use it: Useful when an assistant needs to see or verify what a web page looks like, compare responsive layouts, or pull titles, descriptions, Open Graph tags, favicons, and HTTP status without a full screenshot. It fits visual checks, page monitoring, and feeding page images into a vision model.
Requirements: Runs locally over stdio as a Python package (pip install snapgrab, or snapgrab[mcp] for the MCP server). Requires Python and Playwright's Chromium browser, installed with playwright install chromium. Desktop only; no accounts, API keys, or environment variables are declared.

Before you install

The server fetches arbitrary URLs, so captured pages and their metadata come from third-party sites and requests reveal the target URL to those sites. Screenshots are written to local disk (for example under /tmp/snapgrab). Installing Playwright downloads a browser binary. No credentials are requested.

Installation

In SourceWeft

Open Snapgrab in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Snapgrab

[PyPI] [Python] [License] [Tests]

한국어 문서 · llms.txt

URL to screenshot with metadata. Claude Vision optimized.

python

from snapgrab import capture
result = await capture("https://example.com")print(result.path)           # /tmp/snapgrab/example_com_desktop_20260317_120000.pngprint(result.metadata.title) # "Example Domain"print(result.vision_tokens)  # ~2764

from snapgrab import capture
result = await capture("https://example.com")print(result.path)           # /tmp/snapgrab/example_com_desktop_20260317_120000.pngprint(result.metadata.title) # "Example Domain"print(result.vision_tokens)  # ~2764

Features

Screenshot capture — PNG, JPEG, PDF with full-page support
Page metadata — title, description, Open Graph tags, favicon, HTTP status
Claude Vision optimized — auto-resize to 1568px, token cost estimation
Viewport presets — desktop (1920x1080), tablet (768x1024), mobile (375x812)
MCP server included — 3 tools for Claude Code / MCP clients
Element capture — screenshot specific CSS selectors
Dark mode — force light/dark color scheme

Install

First run will prompt to install Playwright browsers:

Usage

Python API

python

import asynciofrom snapgrab import capture
async def main():    # Basic screenshot    result = await capture("https://example.com")
    # Mobile viewport, full page    result = await capture("https://example.com", viewport="mobile", full_page=True)
    # Dark mode, JPEG format    result = await capture("https://example.com", dark_mode=True, format="jpeg")
    # Specific element    result = await capture("https://example.com", selector="#main-content")
    # Custom viewport    result = await capture("https://example.com", viewport=(1440, 900))
asyncio.run(main())

import asynciofrom snapgrab import capture
async def main():    # Basic screenshot    result = await capture("https://example.com")
    # Mobile viewport, full page    result = await capture("https://example.com", viewport="mobile", full_page=True)
    # Dark mode, JPEG format    result = await capture("https://example.com", dark_mode=True, format="jpeg")
    # Specific element    result = await capture("https://example.com", selector="#main-content")
    # Custom viewport    result = await capture("https://example.com", viewport=(1440, 900))
asyncio.run(main())

CLI

bash

snapgrab https://example.com                          # basic PNGsnapgrab https://example.com -v mobile -f             # mobile, full pagesnapgrab https://example.com --format jpeg -q 90      # JPEG quality 90snapgrab https://example.com -s "#hero" --dark-mode   # element + dark modesnapgrab https://example.com -j                       # JSON outputsnapgrab meta https://example.com                     # metadata only

snapgrab https://example.com                          # basic PNGsnapgrab https://example.com -v mobile -f             # mobile, full pagesnapgrab https://example.com --format jpeg -q 90      # JPEG quality 90snapgrab https://example.com -s "#hero" --dark-mode   # element + dark modesnapgrab https://example.com -j                       # JSON outputsnapgrab meta https://example.com                     # metadata only

MCP Server

Tools:

capture_screenshot — capture URL with metadata and vision token estimate
capture_comparison — compare desktop vs mobile (or any viewports)
extract_page_metadata — metadata only, no screenshot

How It Works

mermaid

flowchart LR    A["🔗 URL"] --> B["Playwright\nlaunch browser"]    B --> C["📸 Screenshot\nfull page / viewport"]    C --> D["Resize for\nClaude Vision"]    D --> E["✅ Image +\nMetadata"]

flowchart LR    A["🔗 URL"] --> B["Playwright\nlaunch browser"]    B --> C["📸 Screenshot\nfull page / viewport"]    C --> D["Resize for\nClaude Vision"]    D --> E["✅ Image +\nMetadata"]

CaptureResult

python

result.path            # saved file pathresult.format          # "png", "jpeg", "pdf"result.width           # viewport widthresult.height          # page height (full_page) or viewport heightresult.file_size       # bytesresult.vision_tokens   # estimated Claude Vision token costresult.vision_path     # path to Vision-optimized image (≤1568px)result.processing_time_msresult.metadata.titleresult.metadata.descriptionresult.metadata.og_titleresult.metadata.og_imageresult.metadata.favicon_urlresult.metadata.status_coderesult.metadata.url    # final URL after redirects

result.path            # saved file pathresult.format          # "png", "jpeg", "pdf"result.width           # viewport widthresult.height          # page height (full_page) or viewport heightresult.file_size       # bytesresult.vision_tokens   # estimated Claude Vision token costresult.vision_path     # path to Vision-optimized image (≤1568px)result.processing_time_msresult.metadata.titleresult.metadata.descriptionresult.metadata.og_titleresult.metadata.og_imageresult.metadata.favicon_urlresult.metadata.status_coderesult.metadata.url    # final URL after redirects

Used in

watchdeck — Web page monitoring with visual diffs and safety guards

License

MIT

_{Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.}

Source: README.md at commit 9985f7c

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v0.1.0LatestSep 16, 2026

Score

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