extract web content

The problem

LLMs often hallucinate website details or fail to access real-time web data accurately. Manually copying and pasting text from URLs into a prompt is slow, loses metadata, and often includes unwanted navigation noise.

What it does

• Fetches raw HTML and converts it into clean, readable text.
• Extracts page titles and structured metadata automatically.
• Retrieves all hyperlinks found within the page content.
• Supports character limits to manage context window tokens.
• Provides structured error handling for rate limits and upstream failures.

Frameworks & tools

This skill interfaces with the extract_web_content MCP server and works with any LLM client supporting MCP tools.

Why this beats prompting it yourself

Directly prompting an LLM to "read a site" usually results in outdated training data or failed browsing attempts. this skill provides a reliable, programmatic bridge to real-time web data with built-in sanitization, ensuring the model receives structured JSON instead of a messy DOM dump.

Use cases

• Extracting article body text for automated summarization.
• Mapping site architecture by retrieving all internal and external links.
• Gathering SEO metadata and titles for competitive analysis.
• Feeding clean website content into RAG pipelines or knowledge bases.

Known limitations

Requires the extract_web_content MCP server to be active. It cannot access pages behind authentication or bypass complex bot detection measures.