MCP Server Fetch Python

Overview

The Tatn MCP Server Fetch Python is a lightweight, opinionated MCP server that turns arbitrary web resources into structured, consumable data for AI assistants. It solves the common pain point of fetching and normalising content from the open web—whether it is simple static pages, dynamic JavaScript‑heavy SPAs, or rich media files—without the need for custom scraping pipelines. By exposing a small set of well‑defined tools, developers can plug the server into Claude or any MCP‑compliant client and let the assistant handle everything from raw text extraction to AI‑powered image analysis.

The server offers four distinct tools, each tailored to a particular use case:

• pulls the raw payload from a URL, ideal for structured formats such as JSON, XML or CSV where no rendering is required.
• spins up a headless browser to retrieve fully rendered DOM content, enabling the assistant to interact with modern JavaScript frameworks and SPAs.
• converts the fetched page into clean, Markdown‑formatted text that preserves headings, lists and links, making it easy to embed in documents or chat logs.
• leverages OpenAI’s vision and OCR capabilities to extract textual information from images or videos, returning a Markdown summary of the visual content.

These tools provide developers with granular control over how web data is ingested, while keeping the interface simple. The server can be launched directly from Claude Desktop via a configuration entry or run locally with a minimal Python environment, and it supports customisation through environment variables such as , and .

Key Advantages

• Zero‑config scraping – No need to write bespoke parsers; the server handles both static and dynamic content out of the box.
• AI‑enhanced media extraction – The vision tool automatically turns images and videos into readable text, freeing developers from manual transcription.
• Consistent output formats – Markdown and raw HTML outputs are predictable, enabling downstream pipelines (e.g., summarisation, translation, or knowledge‑graph ingestion) to work seamlessly.
• Secure integration – By keeping the OpenAI key on the server side, developers avoid exposing credentials to client applications.

Real‑World Use Cases

1. Research assistants can pull scholarly articles or news feeds, convert them to Markdown, and feed the text into summarisation models.
2. Content curators can fetch product pages or marketing sites, render the full page with JavaScript, and extract structured data for cataloguing.
3. Accessibility tools can transform video lectures into transcript‑style Markdown, improving searchability and captioning.
4. Knowledge‑base builders can scrape internal documentation portals, normalise the content, and import it into vector stores for semantic search.

By integrating this MCP server into an AI workflow, developers gain a robust, reusable bridge between the vast ecosystem of web content and intelligent assistants, accelerating development cycles and enhancing data quality.