Overview

mcp‑rquest is a Model Context Protocol (MCP) server that equips Claude and other large language models with realistic, full‑featured HTTP request capabilities. By leveraging the rquest library, it emulates a modern web browser’s network stack—including accurate TLS handshakes and JA3/JA4 fingerprints—so that models can interact with sites that enforce strict anti‑bot checks. This makes it possible to fetch dynamic content, submit forms, and navigate web pages in a way that feels native to the target server, while still keeping all operations under the control of an AI workflow.

The core value proposition is two‑fold. First, developers can expose any HTTP endpoint as a tool that the model can invoke on demand, eliminating manual API wrappers or custom client code. Second, mcp‑rquest automatically transforms complex payloads into formats that are easier for LLMs to consume. HTML responses are converted to Markdown, PDFs are rendered into clean text via the Marker library, and large binary blobs are stored safely in temporary files with token‑aware streaming. This streamlines downstream reasoning, summarization, or data extraction tasks that would otherwise require additional preprocessing steps.

Key capabilities include:

• Comprehensive HTTP support: All standard methods (GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS, TRACE) are available as dedicated tools with fine‑grained control over headers, cookies, redirects, query strings, and payload types (JSON, form data, multipart).
• Browser‑level fingerprinting: TLS and HTTP/2 fingerprints mirror popular browsers, helping bypass CAPTCHAs and rate limits without compromising security.
• Content handling pipelines: Automatic detection of large responses triggers token‑based streaming; HTML and PDF conversions happen on demand, producing clean Markdown ready for LLM ingestion.
• Authentication flexibility: Basic, bearer, and custom auth schemes are supported out of the box.
• Stateful PDF processing: Tools to monitor and restart the internal Marker model loading process ensure reliable conversion even after long sessions.

Typical use cases span web scraping, data collection from protected APIs, and automated browsing for content extraction. For example, a developer can build an assistant that queries a news website, fetches the article HTML, converts it to Markdown, and then asks the model to summarize or answer questions about the content—all within a single MCP workflow. Similarly, PDFs from research repositories can be retrieved and rendered into text without manual downloads or conversions.

Integration is straightforward: the server exposes a set of named tools that Claude can call via the MCP interface. Each tool corresponds to an HTTP operation or a post‑processing step, and the model can chain them together declaratively. Because the server handles all networking intricacies—including SSL, redirects, and content‑type detection—developers can focus on higher‑level logic while the assistant manages reliable, browser‑style communication with external resources.