Extract and clean web content into structured formats optimized for LLM processing. For developers building AI agents, chatbots, or RAG systems that need reliable web scraping without boilerplate.
io.github.agenson-horrowitz/web-content-extractor
Local install
STDIO
No auth required
How models use it and what it is built for.
Extract and clean web content into structured formats optimized for LLM processing. For developers building AI agents, chatbots, or RAG systems that need reliable web scraping without boilerplate.
Local install — runs as a subprocess.
Where to find authoritative docs and source for Agenson Horrowitz Web Content Extractor.
Paste any of these into Agent Studio after connecting Agenson Horrowitz Web Content Extractor.
Common questions about connecting and running Agenson Horrowitz Web Content Extractor.
What web content formats does this MCP server output?
The server extracts and processes web content into clean, structured formats optimized for LLM consumption. Refer to the documentation for specific output schema options (JSON, markdown, etc.).
How do I install and run the web-content-extractor MCP server?
Install via npm with `npx @agenson-horrowitz/web-content-extractor-mcp@1.0.8`. It runs over stdio transport, so configure your MCP client to spawn the process and communicate via standard input/output.
Does this server handle JavaScript-rendered content or only static HTML?
The registry metadata does not specify JavaScript rendering capabilities. Check the full documentation or GitHub repository for details on whether dynamic content is supported.
What authentication or API keys are required?
The install command and registry metadata do not mention API keys or authentication requirements. Consult the project documentation to confirm if any credentials are needed for web requests.
How is this different from using a generic HTTP client or cheerio?
This MCP server is purpose-built to clean and structure web content specifically for LLM consumption, removing boilerplate and formatting data in ways that improve AI model performance—rather than requiring you to write custom parsing logic.
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