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MCP server · AI

Fast Web Fetch

Fast Fetch Web Pages to LLM-ready contexts

Unclaimed last commit 9 months ago ai
34Low

Scored 7 months ago · breakdown

About Fast Web Fetch

Fast Web Fetch is an MCP server published by trotsky1997 in the AI category: fast Fetch Web Pages to LLM-ready contexts.

The repository has 7 stars and 1 forks, with the last commit 9 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

From source
git clone https://github.com/trotsky1997/Fast-Web-Fetch

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

fastwebfetch

Fastwebfetch scrapes web pages with Patchright and converts extracted HTML into cleaned Markdown. It relies on the markdownify, mdformat, tqdm, pylatexenc, and patchright packages.

How to use (one-shot)

The fastest way to run once and get Markdown output:

uv sync
uv run main.py init
uv run main.py "https://example.com" > output.md

If you already ran init, just run:

uv run main.py "https://example.com" > output.md

or without git clone

uvx --from "git+https://github.com/trotsky1997/Fast-Web-Fetch.git" python -m main init
uvx --from "git+https://github.com/trotsky1997/Fast-Web-Fetch.git" python -m main "https://example.com" > output.md

MCP quickstart

Run the MCP server directly from the repo URL:

uvx --from "git+https://github.com/trotsky1997/Fast-Web-Fetch.git" python -m mcp_server

Example client config (stdio server):

{
  "servers": {
    "fastwebfetch": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/trotsky1997/Fast-Web-Fetch.git",
        "python",
        "-m",
        "mcp_server"
      ]
    }
  }
}

Features

  • Patchright-based scraping with a persistent browser profile.
  • Optional paywall bypass via bypass-paywalls-chrome-clean.
  • Markdown cleanup with optional URL stripping and summary mode.

Requirements

  • Python 3.11+ (see .python-version).
  • uv for dependency management (recommended).

uv workflow

  • Install uv (see https://astral.sh/uv for installers) or use pip install uv.
  • Run uv sync to install all dependencies from uv.lock.
  • Use uv run main.py to execute the script with uv-managed dependencies.
  • When dependencies change, run uv lock (or uv lock --upgrade), then commit the updated uv.lock alongside pyproject.toml.

Initialization

  • Run uv run main.py init as a one-shot setup command to install Chromium via Patchright, create the persistent profile directory, and validate that the bundled extensions/bypass-paywalls-chrome-clean assets exist.
  • When the bypass extension is missing, init downloads it automatically from GitFlic; override the URL with FASTWEBFETCH_PAYWALL_URL if needed.
  • The init command accepts the same --browser-data-dir as the scraper, plus --channel (defaults to chromium) to choose the Patchright browser and --paywall-extension-dir when you moved the bypass extension.
  • Skip the automatic browser install with --skip-chrome-install (useful when the requested browser is already available). Re-running init is safe and simply updates the profile directory and reconfirms the assets.

Patchright scraping

  • Install Chrome through patchright with patchright install chrome (recommended) or install Chromium and run uv run main.py init --channel chromium.
  • Run uv run main.py <URL> to scrape a page, convert it to Markdown, and stream the result on stdout; pass --browser-data-dir to reuse a persistent profile and --no-headless to show the UI.
  • The scraper launches Patchright with a persistent context (no_viewport=True) and uses the Chrome channel by default.
  • Enable paywall bypassing with --enable-paywall-bypass, or point to a custom directory via --paywall-extension-dir.
  • Extensions are disabled in headless Chromium/Chrome, so avoid --headless when relying on paywall bypassing.
  • See extensions/bypass-paywalls-chrome-clean/README.md for the paywall extension configuration, updates, and bundled MIT license.
  • URLs are stripped from the markdown output by default to avoid link noise; add --keep-urls if you need to preserve HTTP/HTTPS links in the converted content.
  • Use --summary-only to generate a short model summary instead of full markdown. The default checkpoint is HuggingFaceTB/SmolLM2-135M-Instruct, adjustable via --summary-model.

Quickstart

uv sync
uv run main.py init
uv run main.py "https://example.com"

Development

  • Use uv run pytest for tests once they exist.
  • Format code with uv run black main.py and lint with uv run ruff main.py.

MCP usage

Run via uvx (git path)

Use uvx to run the MCP server directly from a git URL without cloning:

uvx --from "git+https://github.com/trotsky1997/Fast-Web-Fetch.git" python -m mcp_server

Notes:

  • Update the git URL to your fork or a specific ref if needed.
  • uvx resolves dependencies from pyproject.toml in the repo.
  • The server uses a persistent Patchright profile at ~/.fastwebfetch/patchright-profile by default.

MCP config (generic JSON, copy/paste)

Use this JSON in any MCP client that supports stdio servers:

{
  "servers": {
    "fastwebfetch": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/trotsky1997/Fast-Web-Fetch.git",
        "python",
        "-m",
        "mcp_server"
      ]
    }
  }
}

fetch_md tool

The MCP server exposes a single tool named fetch_md with these inputs:

  • url (string, required): URL to scrape.
  • enable_paywall_bypass (boolean, optional): Load the bundled bypass extension.
  • keep_urls (boolean, optional): Keep HTTP/HTTPS URLs in the markdown output.
  • summary_only (boolean, optional): Return a short summary instead of full markdown.

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Fast Web Fetch

How do I install Fast Web Fetch?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Fast Web Fetch safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Fast Web Fetch still maintained?

The last commit was 9 months ago. That's long enough that you should check whether the maintainer is responding to issues before depending on it.