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Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear - MCP Implementation

Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear

Snap_sense captures life’s fleeting moments with crystal-clear precision—never miss a smile, a win, or a milestone. Smart tech that senses your magic. 📸✨

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66% of users reported increased productivity after just one week

About Snap_sense

What is Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear?

Snap_sense is a lightweight MCP-compliant tool that empowers AI applications to automatically capture and return crisp webpage screenshots with zero manual effort. Built on the Model Context Protocol, it acts as a visual middleware between LLMs and external systems, enabling seamless integration into platforms like Claude Desktop or automation workflows. Think of it as the eyes of your AI—capturing moments as if guided by human intuition.

How to use Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear?

  1. Deploy the MCP server using Python 3.11+ and the provided UV setup commands
  2. Configure your AI platform with the server details in claude_desktop_config.json
  3. Issue natural language prompts like "Show me the GitHub repo screenshot" to trigger instant captures

Pro tip: Always verify your AbstractAPI key placement in the .env file before deployment!

Snap_sense Features

Key Features of Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear?

  • Smart Composition: Automatically crops focus areas while maintaining full-page context
  • MCP Native: Built-in protocol compliance ensures zero friction with AI ecosystems
  • Crystal-Clear Output: Optimized rendering for retina displays and modern browsers
  • Development Ready: Pre-configured with Black and Isort for production-grade code

Use cases of Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear?

Perfect for:

  • Automated customer support ticket validation
  • Real-time visual audits during web development
  • Content verification workflows
  • AI-driven report generation with visual evidence

My favorite use case? Automatically documenting dev environments for bug reporting – no more "can you screenshot that?" back-and-forth!

Snap_sense FAQ

FAQ from Snap_sense: Auto-Capture Perfect Moments, Smart & Crystal-Clear?

Does it work with headless browsers?
Yes! Leverages modern headless rendering under the hood
How many concurrent requests can it handle?
Designed for 50+ parallel captures with proper infrastructure
Can I customize capture regions?
Not yet, but it's on the roadmap – watch this space!
What about sensitive data in screenshots?
Implement your own redaction layer via MCP middleware hooks

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.

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

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

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