MCP URL2SNAP
A lightweight MCP server that captures screenshots of any URL and returns the image URL. Requires an AbstractAPI key.
MCP URL2SNAP ππ€
A lightweight Model Context Protocol (MCP) server that enables your LLM to capture screenshots of any specified URL and return only the access URL for the captured image. This tool simplifies the process of generating and sharing webpage snapshots, making it perfect for integrating visual capture capabilities into AI applications like Claude Desktop or automation workflows.
What is Model Context Protocol (MCP)?
At its core, MCP is a standardized protocol designed to streamline communication between AI models and external systems. Think of it as a universal language that allows different AI agents, tools, and services to interact seamlessly.
Features
- Email Verification: Verify email addresses in real-time.
- MCP Integration: Seamlessly connect with MCP-compatible LLMs.
- Easy Setup: Built with Python and the MCP SDK for quick deployment.
MCP follows a client-server architecture:
Watch the Demo
Click the image below to watch a video demo of the MCP Email Verify tool in action:
Requirements
- Python: Python 3.11.0 or higher.
- UV: 0.6.9 or higher.
Setup
Installing via Smithery
To install MCP-URL2SNAP for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @Abhi5h3k/MCP-URL2SNAP --client claude
Manual Installation
1. Clone the Repository
git clone https://github.com/Abhi5h3k/MCP-URL2SNAP.git
cd MCP-URL2SNAP
2. Install UV
If you donβt have UV installed, you can install it using the following commands:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Verify the installation:
uv --version
3. Set Up the Virtual Environment
Create a virtual environment using UV:
uv venv
Activate the virtual environment: On Windows:
.venv\Scripts\activate
4. Install Dependencies Install the required dependencies from pyproject.toml using UV:
uv install
Running the Server
- Set Up Environment Variables Create a .env file in the root directory and add your AbstractAPI key:
ABSTRACT_API_KEY=your_api_key_here
- Run the Server Start the MCP server:
uv run server.py
Usage
- Register the Server with Claude Desktop Update the claude_desktop_config.json file to include your MCP server:
{
"mcpServers": {
"verify_mail": {
"command": "uv",
"args": [
"--directory",
"C:\\ABSOLUTE\\PATH\\TO\\MCP-Email-Verify",
"run",
"server.py"
],
"env":{
"ABSTRACT_API_KEY":"YUR_API_KEY"
}
}
}
}
-
Restart Claude Desktop Restart Claude Desktop to detect the new tool.
-
Verify Emails Use prompts like:
"can you show me the screenshot of https://github.com/Abhi5h3k"
Development
Formatting and Linting This project uses black and isort for code formatting and import sorting.
- Install development dependencies:
uv add black isort --dev - Format the code:
black . - Sort imports:
isort .
Set up pre-commit
pre-commit install
pre-commit run --all-files
Article: Model Context Protocol (MCP): A Beginner's Guide to the Future of AI Communication
Related Servers
Browser Use
An AI-driven browser automation server for natural language control and web research, with CLI access.
MCP Web Research Server
A server for web research that brings real-time information into AI models and researches any topic.
Yahoo Finance
Interact with Yahoo Finance to get stock data, market news, and financial information using the yfinance Python library.
WebforAI Text Extractor
Extracts plain text from web pages using WebforAI.
Read Website Fast
Fast, token-efficient web content extraction that converts websites to clean Markdown. Features Mozilla Readability, smart caching, polite crawling with robots.txt support, and concurrent fetching with minimal dependencies.
Crawl4AI RAG
Integrate web crawling and Retrieval-Augmented Generation (RAG) into AI agents and coding assistants.
YouTube Transcript Extractor
Extracts transcripts from public YouTube videos.
Headline Vibes Analysis
Analyzes the sentiment of news headlines from major US publications using the NewsAPI.
Simple MCP Tool Server
A simple MCP server that provides a tool for fetching website content using SSE transport.
Web Fetch
Fetches and transforms web content, including JavaScript-rendered pages and media files, into various formats.
