An MCP server enabling LLMs to perform HTTP requests, fetch web content, and convert it to Markdown. Supports custom headers, User-Agents, and all key HTTP methods, facilitating flexible content retrieval.
Added on:
Created by:
Apr 08 2025
An MCP server enabling LLMs to perform HTTP requests, fetch web content, and convert it to Markdown. Supports custom headers, User-Agents, and all key HTTP methods, facilitating flexible content retrieval.
Added on:
Created by:
Apr 08 2025
coucya
Featured
What is Web Content Retrieval MCP?
This MCP (Multi-Channel Protocol) server allows Large Language Models (LLMs) to fetch, process, and analyze web content through various HTTP methods. It supports converting web pages into Markdown, filtering out unnecessary tags, and customizing request headers and User-Agents for precise content access. Features include full support for GET, POST, PUT, DELETE, and PATCH methods, access to complete HTTP response headers, and options for raw HTML or cleaned-up content. Designed for seamless integration in applications requiring automated web content extraction, data collection, and content analysis, it enhances the capabilities of LLMs by providing structured and customizable HTTP request functionalities.
Who will use Web Content Retrieval MCP?
- Developers
- Data Analysts
- Content Scrapers
- AI/ML Engineers
- Web Content Managers
How to use the Web Content Retrieval MCP?
- Step1: Clone the repository from GitHub
- Step2: Install dependencies using pip
- Step3: Configure the MCP server with your settings
- Step4: Start the MCP server with Python
- Step5: Use HTTP methods (fetch, get, post, put, delete) to retrieve or send web content
Web Content Retrieval MCP's Core Features & Benefits
The Core Features
- fetch web content with HTML or Markdown output
- execute HTTP GET request
- execute HTTP POST request
- execute HTTP PUT request
- execute HTTP DELETE request
- customize request headers
- set User-Agent strategies
- access HTTP response headers
The Benefits
- headers improve request accuracy
- Ideal for automation and AI integration
Web Content Retrieval MCP's Main Use Cases & Applications
- Automated web scraping for research
- Content extraction for data analysis
- Web page monitoring and updates
- Building custom content aggregators
- Training data collection for AI models
FAQs of Web Content Retrieval MCP
Developer
- coucya
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