Scrapling MCP Server: Adaptive Web Scraping with Scrapy-like Spiders
Scrapling: An Adaptive Web Scraping MCP Server
Scrapling, an AI-category MCP Server written in Python, provides an adaptive web scraping framework designed to manage everything from a single HTTP request to large-scale, persistent crawls. With over 68,000 GitHub stars, it offers a comprehensive set of tools for developers focused on data extraction.
Defining and Running Crawls with Spiders
The core of Scrapling's crawling capabilities lies in its Scrapy-like Spider API. Developers define spiders using start_urls and asynchronous parse callbacks, handling Request and Response objects. This familiar pattern allows for structured definition of scraping logic.
Scrapling supports concurrent crawling with configurable limits, per-domain throttling, and download delays to manage server load and avoid detection. For long-running or complex operations, spiders can leverage multi-session support, routing requests to different HTTP or headless browser sessions by ID. This unified interface simplifies handling diverse fetching requirements within a single spider.
Persistence and Real-time Data Streaming
A significant feature for robust scraping operations is Scrapling's checkpoint-based crawl persistence. Developers can initiate a graceful shutdown (e.g., via Ctrl+C) and later restart the crawl, resuming exactly from where it left off. This prevents data loss and allows for long-running operations to be interrupted and continued.
For real-time data consumption, Scrapling offers a streaming mode. Scraped items can be accessed as they arrive via async for item in spider.stream(), providing real-time statistics. This is particularly useful for integrating scraped data directly into UIs, pipelines, or other live processing systems.
Handling Website Defenses and Development Workflow
Scrapling includes built-in mechanisms to navigate common web scraping challenges. It features automatic detection and retry of blocked requests, with customizable logic to adapt to various blocking strategies. Additionally, an optional robots_txt_obey flag ensures compliance with Disallow, Crawl-delay, and Request-rate directives, utilizing per-domain caching for efficiency.
For development and iteration, Scrapling provides a development mode. This allows developers to cache responses to disk on the first run and replay them on subsequent runs. This significantly speeds up iteration on parse() logic by eliminating the need to re-hit target servers during development, saving time and reducing load on target websites.
Results can be exported through custom hooks and pipelines, or directly using the built-in JSON/JSONL export functionalities via result.items.to_json() or result.items.to_jsonl().