# Scrapling MCP Server: Adaptive Web Scraping with Scrapy-like Spiders

> Scrapling is a Python-based MCP Server for adaptive web scraping, offering a full crawling framework with features like concurrent crawling, multi-session support, and pause/resume capabilities. It's designed for developers needing robust, scalable data extraction.

**Published:** 2026-07-06T12:00:16.842+00:00

**Keywords:** scrapling,mcp-server,d4vinci-scrapling,web-scraping,python-ai

# 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()`.

## References

- [Scrapling on GitHub](https://github.com/D4Vinci/Scrapling)
- [Model Context Protocol Documentation](https://modelcontextprotocol.io/introduction)
- [Scrapling on model-context-protocol.com](https://model-context-protocol.com/servers/)

## Related Repository

- [scrapling](https://model-context-protocol.com/servers/scrapling)

**Source:** https://model-context-protocol.com/blog/scrapling-mcp-server-adaptive-web-scraping-with-scrapy-like-spi-mcp-server-guide