What is Mcp Url2snap

MCP-URL2SNAP is a lightweight Model Context Protocol (MCP) server that allows users to capture screenshots of specified URLs and return the access URL for the captured images. It simplifies the process of generating and sharing webpage snapshots, making it suitable for AI applications and automation workflows.

Use cases

Use cases for MCP-URL2SNAP include generating webpage previews for social media sharing, automating website monitoring tasks, and integrating visual content capture into AI-driven applications.

How to use

To use MCP-URL2SNAP, clone the repository from GitHub, install the required dependencies, and set up the server. After setup, you can input the desired URL to capture a screenshot and receive the access URL for the image.

Key features

Key features of MCP-URL2SNAP include real-time email verification, seamless integration with MCP-compatible LLMs, and an easy setup process using Python and the MCP SDK.

Where to use

MCP-URL2SNAP can be used in various fields such as AI development, web automation, digital marketing, and any application requiring visual capture of web content.

MCP URL2SNAP 🚀🤖

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

1. Clone the Repository

git clone https://github.com/Abhi5h3k/MCP-URL2SNAP.git
cd MCP-URL2SNAP

2. Install UV

If you don’t 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

1. Set Up Environment Variables
   Create a .env file in the root directory and add your AbstractAPI key:

ABSTRACT_API_KEY=your_api_key_here

2. Run the Server
   Start the MCP server:

uv run server.py

Usage

1. 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"
            }
        }
    }
}

2. Restart Claude Desktop
   Restart Claude Desktop to detect the new tool.

3. 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.

1. Install development dependencies:

    uv add black isort --dev
   

2. Format the code:

   black .
   

3. 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