mcpField
S
Snap_sense

Snap_sense

Local
github.com/Abhi5h3kBrowser Automation2Updated 67w ago

About Snap_sense

Snap_sense is a Python MCP server for capturing a screenshot of a specified URL and returning an access URL for the image. It is intended for MCP-compatible AI applications and automation workflows.

Key features

  1. 01Capture a URL screenshot
  2. 02Return the image access URL
  3. 03Python-based MCP server

Use cases

  1. 01View a webpage screenshot through an AI client
  2. 02Add webpage capture to an automation workflow

MCP support

It exposes URL screenshot capture to MCP clients as a tool. The README describes setup for Claude Desktop.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MseeP.ai Security Assessment Badge

MCP URL2SNAP 🚀🤖

smithery badge

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.

MCP drawio (1)


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:

client server drawio


Watch the Demo

Click the image below to watch a video demo of the MCP Email Verify tool in action:

Youtube


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 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
  1. 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"
            }
        }
    }
}

image

image

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

  2. 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

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.