Sandbox
@xxxbrian/mcp-rquest

MCP server for browser-like HTTP requests

mcp-rquest plugs into Claude and other MCP clients as a request tool. It sends HTTP traffic with realistic browser fingerprints, handles common request types and auth, and can store large responses for later retrieval. It also converts HTML and PDF content to Markdown so agents can read it more easily.

48 stars10 forksPythonUpdated 1y ago
Who it's for

Builders who want their agent to fetch sites, submit requests, and read web content in a more browser-like way.

What it delivers

You can have your agent access web pages and documents with less manual copying and fewer anti-bot blocks.

What it does

Browser-style HTTP requests

Supports GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS, and TRACE with realistic TLS, JA3, JA4, and HTTP/2 fingerprints.

Response storage and retrieval

Stores large responses in temp directories and lets you fetch them later, including line ranges.

HTML and PDF to Markdown

Converts HTML and PDF responses to Markdown with `get_stored_response_with_markdown` for easier LLM reading.

Request customization

Handles headers, cookies, redirects, query parameters, JSON, form data, multipart uploads, and authentication.

Model state tools

Includes `get_model_state` and `restart_model_loading` for the PDF conversion pipeline.

How to get it

  1. 1Alternatively you can install mcp-rquest via pip
    pip install mcp-rquest
  2. 2After installation, you can run it as a script using
    python -m mcp_rquest

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.

Files in the repo

Repository payload11 top-level entries
  • mcp_rquest
  • .bumpversion.toml
  • .gitignore
  • .python-version
  • LICENSE.txt
  • Makefile
  • pyproject.toml
  • pytest.ini
  • README.md
  • setup.py
  • uv.lock

Discussion (0)

Ask about usage, or say what you built with it

Sign in to join the discussion.

No comments yet. Be the first to say what this is good for.

More connectors

Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface

86k

High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

43k

Universal provider proxy for OpenAI Codex & Claude Code — use any LLM (Claude, Gemini, Grok, DeepSeek, Ollama…) with Codex CLI, App, SDK, and Claude Code

14k
okf-memory/
okf-agent-memory

Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.

547
tirth8205/
code-review-graph

Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.

31k
2akouwu/
reverify

Stop your AI from making things up — it proposes, deterministic tools decide, every claim checked against ground truth with evidence. Grounded facts and context survive resets. Reverse engineering is the proving ground. MCP server + CLI.

1.1k