Web Analyzer

This project is a web analysis summary MCP server project.

python

2

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "kimdonghwi94-web-analyzer-mcp": {
      "command": "uv",
      "args": [
        "run",
        "mcp-webanalyzer"
      ],
      "env": {
        "OPENAI_MODEL": "gpt-4",
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}

You run an MCP server that analyzes and summarizes web content using smart extraction and AI-powered Q&A. This server focuses on turning complex web pages into clean, analysis-ready Markdown and answering questions about page content on demand.

How to use

To run the Web Analyzer MCP server and connect with your MCP client, start the local stdio server and then interact with its tools from your client of choice. You can extract organized markdown from web pages or ask questions about page content using the integrated AI Q&A feature.

Basic usage patterns you can perform with your MCP client include:

Extract clean markdown from a URL using the server tool url_to_markdown to focus on essential content like tables, images, and key text.
Ask targeted questions about a page’s content using web_content_qna to get AI-generated answers based on the page material.

Code and commands you will use

# Start the Web Analyzer MCP server locally (stdio mode)
uv run mcp-webanalyzer

Available tools

url_to_markdown

Converts a web page to clean markdown with essential content kept and formatting preserved for analysis.

web_content_qna

AI-powered Q&A that extracts relevant content chunks and generates answers based on page content.