MCP Web Scraper

The MCP Web Scrapper is a focused tool that automates the discovery and extraction of Model Context Protocol (MCP) servers hosted on the Glama.ai platform. By crawling the public-facing site, it compiles a comprehensive list of MCP server URLs and then visits each link to harvest metadata such as server name, description, available resources, prompts, tools, and sampling parameters. The result is a structured repository of MCP server data that developers can query programmatically, enabling quick onboarding and integration into AI workflows without manual browsing.

For developers building AI assistants that need to discover or evaluate external MCP services, this server solves the tedious and error‑prone task of manually cataloguing each available endpoint. Instead of sifting through web pages or relying on incomplete documentation, the scraper produces a ready‑made dataset that can be imported into search indices, recommendation engines, or automated testing pipelines. This streamlines the process of selecting appropriate MCP servers for specific use cases and ensures that the data remains up‑to‑date as new servers are added or existing ones evolve.

Key capabilities of the MCP Web Scrapper include:

• Automated link harvesting – a headless browser navigates the Glama.ai listings, collecting every MCP server URL into a single JSON file.
• Deep data extraction – for each link, the scraper pulls structured details such as server name, description, resource list, tool definitions, prompt templates, and sampling options.
• Progress reporting – the console output shows real‑time progress, making it easy to monitor long runs and identify stalls.
• Data persistence – extracted information is saved in a dedicated folder, with each server’s data stored in its own file named after the MCP service. This modular layout facilitates incremental updates and selective re‑processing.

Typical use cases include:

• Cataloging for AI marketplaces – aggregating MCP servers into a searchable catalog that AI assistants can query to suggest relevant services.
• Automated testing suites – feeding the scraped data into test harnesses that verify server compliance with MCP specifications.
• Dynamic onboarding – allowing new developers to programmatically discover and register available MCP servers in their own infrastructure.
• Analytics dashboards – feeding the data into BI tools to track popularity, feature sets, or usage patterns across the MCP ecosystem.

Integration with existing AI workflows is straightforward: once the JSON repository is generated, any MCP‑aware client can load it into memory or a database and expose the servers as selectable options. Because the scraper updates automatically, developers can schedule nightly runs to keep their catalogs fresh without manual intervention. The result is a reliable, scalable foundation for building AI assistants that seamlessly connect to a diverse array of external tools and data sources.