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mcp-rquest

MCP.Pizza Chef: xxxbrian

mcp-rquest is an MCP server that provides advanced HTTP request capabilities with realistic browser emulation, including accurate TLS/JA3/JA4 fingerprints to bypass anti-bot protections. It supports converting PDF and HTML documents to Markdown, facilitating easier processing by LLMs like Claude, GPT-4, and Gemini. Built on the rquest library, it enables models to interact naturally with web environments and extract structured content for AI workflows.

Use This MCP server To

Bypass anti-bot measures with realistic HTTP requests Convert PDFs to Markdown for LLM processing Convert HTML documents to Markdown for easier analysis Enable LLMs to interact naturally with websites Extract structured web content for AI workflows Simulate browser TLS fingerprints in HTTP requests

README

mcp-rquest

PyPI Version Python Versions GitHub Stars License

A Model Context Protocol (MCP) server that provides advanced HTTP request capabilities for Claude and other LLMs. Built on rquest, this server enables realistic browser emulation with accurate TLS/JA3/JA4 fingerprints, allowing models to interact with websites more naturally and bypass common anti-bot measures. It also supports converting PDF and HTML documents to Markdown for easier processing by LLMs.

Features

  • Complete HTTP Methods: Support for GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS, and TRACE
  • Browser Fingerprinting: Accurate TLS, JA3/JA4, and HTTP/2 browser fingerprints
  • Content Handling:
    • Automatic handling of large responses with token counting
    • HTML to Markdown conversion for better LLM processing
    • PDF to Markdown conversion using the Marker library
    • Secure storage of responses in system temporary directories
  • Authentication Support: Basic, Bearer, and custom authentication methods
  • Request Customization:
    • Headers, cookies, redirects
    • Form data, JSON payloads, multipart/form-data
    • Query parameters
  • SSL Security: Uses BoringSSL for secure connections with realistic browser fingerprints

Available Tools

  • HTTP Request Tools:

    • http_get - Perform GET requests with optional parameters
    • http_post - Submit data via POST requests
    • http_put - Update resources with PUT requests
    • http_delete - Remove resources with DELETE requests
    • http_patch - Partially update resources
    • http_head - Retrieve only headers from a resource
    • http_options - Retrieve options for a resource
    • http_trace - Diagnostic request tracing
  • Response Handling Tools:

    • get_stored_response - Retrieve stored large responses, optionally by line range
    • get_stored_response_with_markdown - Convert HTML or PDF responses to Markdown format for better LLM processing
    • get_model_state - Get the current state of the PDF models loading process
    • restart_model_loading - Restart the PDF models loading process if it failed or got stuck

PDF Support

mcp-rquest now supports PDF to Markdown conversion, allowing you to download PDF files and convert them to Markdown format that's easy for LLMs to process:

  1. Automatic PDF Detection: PDF files are automatically detected based on content type
  2. Seamless Conversion: The same get_stored_response_with_markdown tool works for both HTML and PDF files
  3. High-Quality Conversion: Uses the Marker library for accurate PDF to Markdown transformation
  4. Optimized Performance: Models are pre-downloaded during package installation to avoid delays during request processing

Installation

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run mcp-rquest.

Using pip

Alternatively you can install mcp-rquest via pip:

pip install mcp-rquest

After installation, you can run it as a script using:

python -m mcp_rquest

Configuration

Configure for Claude.app

Add to your Claude settings:

Using uvx:

{
  "mcpServers": {
    "http-rquest": {
      "command": "uvx",
      "args": ["mcp-rquest"]
    }
  }
}

Using pip:

{
  "mcpServers": {
    "http-rquest": {
      "command": "python",
      "args": ["-m", "mcp_rquest"]
    }
  }
}

Using pipx:

{
  "mcpServers": {
    "http-rquest": {
      "command": "pipx",
      "args": ["run", "mcp-rquest"]
    }
  }
}

Browser Emulation

mcp-rquest leverages rquest's powerful browser emulation capabilities to provide realistic browser fingerprints, which helps bypass bot detection and access content normally available only to standard browsers. Supported browser fingerprints include:

  • Chrome (multiple versions)
  • Firefox
  • Safari (including iOS and iPad versions)
  • Edge
  • OkHttp

This ensures that requests sent through mcp-rquest appear as legitimate browser traffic rather than bot requests.

Development

Setting up a Development Environment

  1. Clone the repository
  2. Create a virtual environment using uv:
    uv venv
  3. Activate the virtual environment:
    # Unix/macOS
    source .venv/bin/activate
    # Windows
    .venv\Scripts\activate
  4. Install development dependencies:
    uv pip install -e ".[dev]"

Acknowledgements

  • This project is built on top of rquest, which provides the advanced HTTP client with browser fingerprinting capabilities.
  • rquest is based on a fork of reqwest.

mcp-rquest FAQ

How does mcp-rquest bypass anti-bot measures?
It uses accurate TLS/JA3/JA4 fingerprints and browser-like HTTP requests to mimic real user traffic, helping bypass common anti-bot protections.
Can mcp-rquest convert documents for LLM consumption?
Yes, it converts PDF and HTML documents into Markdown format for easier parsing by LLMs like Claude, GPT-4, and Gemini.
Is mcp-rquest compatible with multiple LLM providers?
Yes, it supports integration with various LLMs including Claude, GPT-4, and Gemini.
What underlying technology does mcp-rquest use?
It is built on the rquest library, which provides the core HTTP request and fingerprinting capabilities.
Can mcp-rquest simulate browser TLS fingerprints?
Yes, it accurately simulates TLS/JA3/JA4 fingerprints to emulate real browser network behavior.
How does mcp-rquest improve web interaction for models?
By enabling realistic HTTP requests and document conversions, it allows models to interact more naturally and extract meaningful data from web sources.