scrape

Extract data from any website using Scrapling's MCP tools. Bypasses Cloudflare, handles dynamic JS-rendered pages, supports CSS selectors to pre-filter content (saves tokens).

Usage

/scrape <url>
/scrape <url> with selector .article-content
/scrape stealth <url>
/scrape bulk <url1> <url2> <url3>

When to Use (Tool Selection)

| Scenario | Tool | Why | |----------|------|-----| | Simple page, no anti-bot | scrapling_get | Fastest. HTTP with browser TLS fingerprint | | JS-rendered / SPA content | scrapling_fetch | Uses real Chromium browser | | Cloudflare / anti-bot protected | scrapling_stealthy_fetch | Stealth mode, solves captchas | | Multiple pages, same pattern | scrapling_bulk_get / scrapling_bulk_fetch | Parallel processing |

Implementation

Step 1: Parse User Intent

Determine from the user's request:

• URL(s) to scrape
• CSS selector (if specified — e.g., .main-content, #article, table.data)
• Stealth needed? (Cloudflare sites, login-protected, anti-bot mentions)
• Bulk? (multiple URLs)

Step 2: Choose the Right Scrapling MCP Tool

Default path (try in order, escalate on failure):

1. Start with scrapling_get — fast HTTP, handles most sites
2. If content is empty/blocked → escalate to scrapling_fetch (real browser)
3. If still blocked → escalate to scrapling_stealthy_fetch (stealth mode)

User explicitly asks for stealth: Go straight to scrapling_stealthy_fetch

Multiple URLs: Use the bulk_ variants for parallel processing

Step 3: Call the MCP Tool

Use the Scrapling MCP server tools. All tools accept:

• url (required): The URL to scrape
• css_selector (optional): CSS selector to extract specific elements — always use this when possible to reduce token consumption

Single page:

Call scrapling MCP tool: get
Arguments: { "url": "<url>", "css_selector": "<selector if provided>" }

Stealth:

Call scrapling MCP tool: stealthy_fetch
Arguments: { "url": "<url>", "css_selector": "<selector if provided>" }

Bulk:

Call scrapling MCP tool: bulk_get
Arguments: { "urls": ["<url1>", "<url2>"], "css_selector": "<selector>" }

Step 4: Process Results

The MCP returns extracted content (HTML or text depending on selector).

If user wants raw data: Present it formatted If user wants summary: Summarize the extracted content If user wants vault storage: Save to 00-Inbox/Scrape - [Title].md:

markdown
# [Page Title]

**Source:** [URL]
**Scraped:** YYYY-MM-DD
**Selector:** [CSS selector used, if any]

## Content

[Extracted content]

Step 5: Handle Failures

| Error | Action | |-------|--------| | Empty content | Escalate to next fetcher tier | | Connection refused | Check URL is valid | | Cloudflare challenge | Auto-escalate to stealthy_fetch | | Timeout | Retry with longer timeout, suggest fetch for slow JS sites |

Smart Defaults

• News/blog articles: Auto-suggest article, .post-content, .entry-content selectors
• Product pages: Auto-suggest .product, .price, .description selectors
• Tables: Auto-suggest table selector, offer to convert to markdown table
• Lists: Auto-suggest ul, ol selectors

Examples

User: /scrape https://example.com/blog/ai-trends
→ Use scrapling_get, auto-detect article content

User: /scrape stealth https://protected-site.com/data
→ Use scrapling_stealthy_fetch with Cloudflare bypass

User: scrape this page and grab just the pricing table: https://saas.com/pricing
→ Use scrapling_get with css_selector="table" or ".pricing"

User: scrape these 5 competitor pages and compare their features
→ Use scrapling_bulk_get, extract feature lists, present comparison

Prerequisites

Scrapling must be installed with MCP support:

bash
pip install "scrapling[ai]"
scrapling install

The scrapling MCP server must be configured in .mcp.json.

Relationship to Other Tools

• WebFetch (native): Basic URL fetch, no anti-bot, no selectors. Use for simple known-good pages.
• Firecrawl MCP: Cloud-based, requires API key, good for crawling entire sites. Use when you need recursive crawl.
• Scrapling: Local, free, stealthy, selector-based. Default choice for single-page or small-batch scraping.
• Apify: Marketplace of specialized scrapers. Use for platform-specific extraction (LinkedIn, Twitter, etc.)