{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-d4vinci-scrapling","slug":"mcp-d4vinci-scrapling","name":"Scrapling","type":"repo","url":"https://github.com/D4Vinci/Scrapling","page_url":"https://unfragile.ai/mcp-d4vinci-scrapling","categories":["data-pipelines"],"tags":["ai","ai-scraping","automation","crawler","crawling","crawling-python","data","data-extraction","mcp","mcp-server","playwright","python","scraping","selectors","stealth","web-scraper","web-scraping","web-scraping-python","webscraping","xpath"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-d4vinci-scrapling__cap_0","uri":"capability://data.processing.analysis.progressive.http.to.browser.fetcher.hierarchy.with.unified.response.interface","name":"progressive http-to-browser fetcher hierarchy with unified response interface","description":"Implements a three-tier fetcher system (Fetcher → BrowserFetcher → StealthyFetcher) where each level adds capabilities while maintaining identical Response object contracts. All fetchers return Response objects that inherit from Selector, enabling developers to write parsing code once and switch fetching strategies without refactoring. Uses lazy imports via __getattr__ to defer loading heavy dependencies (Playwright, browser engines) until first access, reducing initial import overhead.","intents":["Start with fast static HTTP requests and upgrade to browser automation only when JavaScript rendering is required","Switch between fetching strategies without rewriting parsing logic","Minimize memory footprint and startup latency in resource-constrained environments"],"best_for":["Data engineers building adaptive scraping pipelines that handle both static and dynamic content","Teams migrating from single-strategy scrapers to multi-strategy frameworks","Developers optimizing for latency-sensitive applications where HTTP-only requests should be preferred"],"limitations":["Lazy imports add ~50-100ms to first access of browser-dependent classes","Response interface abstraction may hide fetcher-specific optimizations (e.g., HTTP connection pooling details)","Browser sessions require explicit lifecycle management; no automatic cleanup on exception"],"requires":["Python 3.10+","Playwright library for browser automation (optional until BrowserFetcher is used)","httpx or requests library for static HTTP fetching"],"input_types":["URL strings","HTTP request objects with headers/cookies","Browser configuration dictionaries"],"output_types":["Response objects (unified interface)","Parsed HTML/DOM trees","Selector-chainable query results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_1","uri":"capability://data.processing.analysis.adaptive.element.relocation.and.dynamic.selector.recovery","name":"adaptive element relocation and dynamic selector recovery","description":"Automatically relocates DOM elements when page structure changes during interaction, using fallback selector strategies (CSS → XPath → text content matching) to recover element references after JavaScript mutations. Implements element caching with invalidation detection to identify when selectors no longer match their original targets, then attempts recovery using alternative selector types or proximity-based matching. This enables robust scraping of single-page applications where DOM structure shifts during user interactions.","intents":["Scrape dynamic single-page applications where clicking elements causes DOM restructuring","Maintain element references across JavaScript-driven page mutations without manual selector updates","Handle applications that render content conditionally or reorder DOM nodes during interactions"],"best_for":["Web scraping teams targeting modern React/Vue/Angular applications with dynamic rendering","Automation engineers building resilient workflows for SPA interactions","Data extraction pipelines that must survive page mutations without human intervention"],"limitations":["Fallback selector strategies add ~100-300ms per element relocation attempt","Text content matching fails on dynamically generated or identical text nodes","Proximity-based recovery unreliable if multiple similar elements exist in DOM","No support for elements that are completely removed and re-rendered with different attributes"],"requires":["BrowserFetcher or StealthyFetcher (static HTTP fetcher cannot detect DOM mutations)","Playwright browser instance with DOM inspection capabilities","CSS and XPath selector knowledge for fallback strategy configuration"],"input_types":["Selector strings (CSS or XPath)","Element references from previous queries","DOM mutation event streams"],"output_types":["Relocated element references","Recovery status indicators","Fallback selector chains used"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_10","uri":"capability://data.processing.analysis.custom.type.handlers.and.response.converters.for.structured.data.extraction","name":"custom type handlers and response converters for structured data extraction","description":"Response factory and converter system enables custom type handlers that transform raw HTML into structured Python objects (dataclasses, Pydantic models, TypedDicts). Converters can be registered per-response-type, enabling automatic deserialization of HTML into domain-specific types. Supports chaining converters for multi-step transformations (HTML → intermediate dict → final dataclass). Integrates with Spider framework's Item system for declarative data extraction pipelines.","intents":["Automatically convert extracted HTML data into typed Python objects (dataclasses, Pydantic models)","Define reusable converters for common data extraction patterns","Validate extracted data against schema during conversion"],"best_for":["Data teams building type-safe data extraction pipelines","Developers using Pydantic or dataclasses for data validation","Teams requiring automatic schema validation during extraction"],"limitations":["Custom converters require boilerplate code; no automatic type inference from HTML structure","Converter chaining adds ~50-100ms per conversion step","Validation errors in converters are not automatically retried; requires explicit error handling","No built-in converters for common formats (JSON-LD, microdata); requires custom implementation"],"requires":["Response object from fetcher","Custom converter functions or classes","Target type definitions (dataclass, Pydantic model, etc.)"],"input_types":["Response objects with HTML content","Converter function signatures","Target type definitions"],"output_types":["Typed Python objects (dataclasses, Pydantic models)","Validation error messages","Conversion status indicators"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_11","uri":"capability://automation.workflow.browser.configuration.and.resource.management.with.tab.pooling","name":"browser configuration and resource management with tab pooling","description":"Browser configuration system (BrowserConfig) manages Playwright browser lifecycle, context creation, and tab pooling. Supports headless/headed mode, viewport configuration, device emulation, and custom launch arguments. Tab pooling within a single browser context reduces memory overhead compared to per-request browser spawning. Implements resource cleanup with context managers and automatic tab reuse across requests. Supports browser-specific features like geolocation spoofing, timezone configuration, and locale emulation for testing localized content.","intents":["Configure browser behavior (headless mode, viewport, device emulation) for consistent scraping","Optimize memory usage by pooling tabs within a single browser context","Emulate different devices, locales, and timezones for localized content scraping"],"best_for":["Teams building large-scale browser-based crawlers with memory constraints","Developers testing localized content across different regions and devices","Researchers evaluating how websites behave under different browser configurations"],"limitations":["Tab pooling requires explicit lifecycle management; leaked tabs consume memory indefinitely","Browser launch overhead (~500ms-2s per browser instance) makes per-request browser spawning impractical","Device emulation is not perfect; some sites detect Playwright via timing or feature detection","Geolocation and timezone spoofing can be detected via JavaScript APIs","No support for browser extensions; limited to Playwright's built-in capabilities"],"requires":["BrowserConfig class from scrapling.engines","Playwright browser instance","Sufficient system memory for browser processes (~100-200MB per tab)"],"input_types":["BrowserConfig objects with launch arguments","Viewport dimensions","Device emulation profiles","Geolocation and timezone specifications"],"output_types":["Configured browser instances","Browser context objects","Tab references from pool"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_12","uri":"capability://automation.workflow.cli.and.interactive.shell.for.exploratory.scraping.and.debugging","name":"cli and interactive shell for exploratory scraping and debugging","description":"Command-line interface and interactive shell enable exploratory scraping without writing code. CLI supports single-request scraping with selector extraction (scrapling fetch URL --selector 'div.item'). Interactive shell provides REPL-like environment where users can iteratively test selectors, refine queries, and inspect responses. Shell maintains session state across commands, enabling multi-step workflows (fetch → inspect → extract). Supports command history, tab completion, and pretty-printing of HTML and extracted data.","intents":["Quickly test selectors and scraping logic without writing Python code","Debug selector issues by interactively inspecting HTML structure","Prototype scraping workflows before implementing in code"],"best_for":["Data analysts and non-developers exploring web content","Developers debugging selector issues and testing extraction logic","Teams prototyping scraping workflows before full implementation"],"limitations":["CLI limited to single-request scraping; no multi-step workflows","Interactive shell requires terminal with ANSI color support; limited on Windows","No persistent session storage; shell state is lost on exit","Tab completion requires manual configuration per-shell instance","Pretty-printing large HTML documents can be slow and hard to read"],"requires":["Python 3.10+ with CLI tools installed","Terminal with ANSI color support (optional but recommended)","Network connectivity to target websites"],"input_types":["URL strings (CLI and shell)","CSS/XPath selector strings (CLI and shell)","Shell commands (fetch, inspect, extract, etc.)"],"output_types":["Formatted HTML output","Extracted text and attributes","Pretty-printed JSON data"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_2","uri":"capability://safety.moderation.stealth.browser.automation.with.anti.detection.evasion","name":"stealth browser automation with anti-detection evasion","description":"StealthyFetcher layer applies multiple anti-bot detection evasion techniques including user-agent randomization, header spoofing, WebDriver property masking, and behavioral mimicry (random delays, mouse movements, viewport variations). Uses Playwright's stealth plugin architecture to inject JavaScript that masks automation indicators (navigator.webdriver, chrome.runtime detection) and simulates human-like interaction patterns. Integrates with proxy rotation to distribute requests across IP addresses, making detection by rate-limiting or IP-based blocking more difficult.","intents":["Scrape websites with aggressive bot detection that block automated browsers","Evade WebDriver detection and Cloudflare/similar anti-bot systems","Maintain long-running scraping sessions without triggering rate-limit blocks"],"best_for":["Security researchers and penetration testers evaluating anti-bot defenses","Data teams scraping high-value content from protected websites","Developers building resilient crawlers for competitive intelligence or price monitoring"],"limitations":["Stealth techniques are arms-race dependent; detection systems evolve faster than evasion can be updated","Behavioral mimicry (random delays) adds 2-10 seconds per request, reducing throughput by 80-90%","Proxy rotation requires external proxy infrastructure; free proxies are unreliable and often blacklisted","JavaScript injection for WebDriver masking fails on sites with strict Content Security Policy","No guarantee against fingerprinting via canvas, WebGL, or timing-based detection"],"requires":["StealthyFetcher class (not available in base Fetcher)","Playwright browser instance","Proxy list or proxy service API (optional but recommended)","Understanding of target site's detection mechanisms"],"input_types":["URL strings","Proxy configuration objects","User-agent strings or randomization rules","Behavioral delay parameters"],"output_types":["Response objects with stealth headers applied","Proxy rotation metadata","Detection evasion status indicators"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_3","uri":"capability://data.processing.analysis.unified.html.parsing.with.css.and.xpath.selector.chaining","name":"unified html parsing with css and xpath selector chaining","description":"Response objects inherit from Selector class, providing chainable CSS and XPath query methods that work identically across all fetcher types. Selectors return lists of elements that can be further queried, enabling fluent API patterns like response.css('div.item').xpath('.//span[@class=\"price\"]').text(). Supports both string selectors and compiled selector objects for performance optimization. Parsing is lazy-evaluated; selectors are not executed until .text(), .attr(), or .html() is called, reducing memory overhead for large documents.","intents":["Extract structured data from HTML using familiar CSS and XPath syntax","Chain multiple selectors to navigate complex DOM hierarchies","Optimize parsing performance by deferring selector evaluation until data is actually needed"],"best_for":["Data extraction engineers familiar with CSS/XPath from BeautifulSoup or Scrapy","Teams building data pipelines that require both simple and complex selector patterns","Developers optimizing for memory usage in large-scale crawls"],"limitations":["Lazy evaluation requires explicit terminal operations (.text(), .attr()); forgetting them silently returns empty results","XPath performance degrades on deeply nested documents (>1000 levels)","CSS selector support limited to CSS3 spec; no vendor-specific pseudo-selectors","Chaining selectors across different types (CSS → XPath) requires explicit conversion"],"requires":["Response object from any fetcher (Fetcher, BrowserFetcher, StealthyFetcher)","HTML content in response body","Knowledge of CSS3 or XPath 1.0 syntax"],"input_types":["CSS selector strings (e.g., 'div.item > span.price')","XPath expressions (e.g., '//div[@class=\"item\"]//span[@class=\"price\"]')","Compiled selector objects"],"output_types":["Element lists (chainable Selector objects)","Text content strings","HTML attribute values","Raw HTML fragments"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_4","uri":"capability://memory.knowledge.session.based.connection.and.browser.pooling.with.state.management","name":"session-based connection and browser pooling with state management","description":"Sessions (Session, AsyncSession, BrowserSession) manage connection reuse and browser lifecycle, with browser sessions supporting tab pooling to optimize resource usage. Sessions maintain cookies, headers, and authentication state across multiple requests, enabling workflows that require login or multi-step interactions. Browser sessions pool Playwright tabs within a single browser context, reducing memory overhead compared to spawning separate browser instances. Sessions support proxy assignment per-request or per-session, with automatic rotation strategies.","intents":["Maintain authentication state across multiple requests without re-logging in","Reuse browser instances and tabs to reduce memory overhead in concurrent crawls","Manage cookies and headers consistently across a sequence of related requests"],"best_for":["Web scraping teams building authenticated crawlers for member-only content","Developers optimizing resource usage in high-concurrency scraping scenarios","Teams implementing multi-step workflows (login → browse → extract)"],"limitations":["Browser session tab pooling requires explicit tab lifecycle management; leaked tabs consume memory","Cookie persistence not automatic; requires explicit session save/load for cross-process workflows","Proxy rotation at session level may cause inconsistent IP addresses within a single workflow","No built-in session serialization; state cannot be easily persisted to disk or database"],"requires":["Session class from scrapling.sessions","For browser sessions: Playwright browser instance","For authenticated workflows: valid credentials or session tokens"],"input_types":["URL strings","HTTP headers dictionaries","Cookie objects","Proxy configuration","Browser context options"],"output_types":["Response objects with session state applied","Cookie and header state snapshots","Browser tab references"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_5","uri":"capability://automation.workflow.spider.framework.for.declarative.crawl.workflows.with.request.response.pipelines","name":"spider framework for declarative crawl workflows with request/response pipelines","description":"Spider framework provides a declarative pattern for defining crawl workflows using start_urls, parse() callbacks, and request/response pipelines. Spiders inherit from BaseSpider and define parse() methods that yield Request objects for follow-up crawls or Item objects for data extraction. The framework handles request queuing, deduplication, and response routing automatically. Supports middleware-style request/response processors that can modify requests before sending or transform responses before parsing, enabling cross-cutting concerns like rate-limiting, error handling, and data validation.","intents":["Define large-scale crawl workflows declaratively without manual request queuing","Implement request deduplication and duplicate URL filtering automatically","Apply middleware-style request/response transformations across all requests in a crawl"],"best_for":["Data teams building large-scale crawlers with hundreds of thousands of URLs","Developers implementing complex crawl logic with request deduplication and filtering","Teams requiring middleware-style request/response processing (rate-limiting, retry logic)"],"limitations":["Spider framework requires explicit yield statements; imperative code is harder to debug than declarative configuration","Request deduplication uses URL-based hashing; query parameter order variations create duplicates","No built-in distributed crawling; all requests queued in-process memory","Middleware pipeline adds ~10-20ms per request for processor chain execution","Error handling in parse() callbacks requires explicit try/except; no global error recovery"],"requires":["BaseSpider class from scrapling.spiders","Fetcher instance (HTTP, Browser, or Stealth)","Understanding of generator-based request/response patterns"],"input_types":["start_urls list","Request objects with URL and metadata","Response objects from fetcher"],"output_types":["Item objects (extracted data)","Request objects (follow-up crawls)","Crawl statistics and metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_6","uri":"capability://automation.workflow.wait.strategies.and.page.load.condition.handling.for.dynamic.content","name":"wait strategies and page load condition handling for dynamic content","description":"Implements configurable wait strategies (WaitForSelector, WaitForNavigation, WaitForFunction, WaitForTimeout) that pause execution until specific page load conditions are met. Strategies can be combined (e.g., wait for selector AND navigation) to handle complex loading patterns. Uses Playwright's built-in wait mechanisms (page.wait_for_selector, page.wait_for_navigation) under the hood, with timeout configuration and exception handling. Enables scraping of content that loads asynchronously after initial page render, such as infinite-scroll feeds or lazy-loaded images.","intents":["Wait for dynamically loaded content to appear before attempting to extract data","Handle infinite-scroll pages by waiting for new content to load after scrolling","Detect and wait for page navigation events before proceeding with extraction"],"best_for":["Web scraping teams targeting modern JavaScript-heavy applications with async content loading","Developers building crawlers for infinite-scroll social media feeds or e-commerce sites","Teams requiring robust handling of variable page load times"],"limitations":["Wait timeouts are global; no per-element timeout granularity","WaitForFunction requires JavaScript knowledge; custom wait conditions are error-prone","Excessive wait strategies add 5-30 seconds per page, reducing throughput significantly","No intelligent backoff; failed waits timeout at fixed intervals rather than adapting to page behavior","Race conditions possible if multiple elements match selector before expected content loads"],"requires":["BrowserFetcher or StealthyFetcher (static HTTP fetcher cannot wait for dynamic content)","Playwright browser instance","Knowledge of CSS selectors or JavaScript for custom wait conditions"],"input_types":["CSS selector strings for WaitForSelector","JavaScript function code for WaitForFunction","Timeout values in milliseconds","Navigation URL patterns"],"output_types":["Response objects after wait condition is satisfied","Timeout exceptions if wait condition not met","Page state snapshots after wait completion"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_7","uri":"capability://tool.use.integration.proxy.management.and.rotation.with.per.request.assignment","name":"proxy management and rotation with per-request assignment","description":"Proxy management system supports per-request and per-session proxy assignment with automatic rotation strategies (round-robin, random, weighted). Proxies can be configured as simple URL strings or as ProxyConfig objects with authentication credentials. Rotation happens transparently at the fetcher level without application code changes. Integrates with both static HTTP fetchers (via httpx proxy support) and browser fetchers (via Playwright proxy configuration). Supports proxy health checking and automatic fallback to direct connection if proxy fails.","intents":["Distribute requests across multiple proxy servers to avoid IP-based rate limiting","Rotate proxies automatically without manual request-level configuration","Handle proxy authentication and fallback gracefully when proxies become unavailable"],"best_for":["Data teams scraping at scale with IP-based rate limiting concerns","Developers building resilient crawlers that must survive proxy failures","Teams managing large proxy pools and requiring automatic rotation"],"limitations":["Proxy rotation adds ~100-500ms per request due to proxy negotiation overhead","Round-robin rotation may cause uneven load distribution if proxies have different speeds","Proxy health checking requires additional requests; no passive health detection","Authentication credentials stored in memory; no encrypted credential storage","No support for SOCKS proxies; only HTTP/HTTPS proxies supported"],"requires":["Proxy list or proxy service API","Proxy URL strings or ProxyConfig objects with optional authentication","For browser proxies: Playwright browser instance"],"input_types":["Proxy URL strings (e.g., 'http://proxy.example.com:8080')","ProxyConfig objects with authentication","Rotation strategy enums (ROUND_ROBIN, RANDOM, WEIGHTED)","Proxy health check configuration"],"output_types":["Response objects with proxy metadata","Proxy rotation statistics","Health check status indicators"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_8","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.agent.tool.calling","name":"mcp server integration for ai agent tool calling","description":"Scrapling exposes web scraping capabilities as an MCP (Model Context Protocol) server, enabling AI agents and LLMs to invoke scraping operations through standardized tool-calling interfaces. The MCP server wraps Fetcher, BrowserFetcher, and StealthyFetcher as callable tools with schema-based function signatures, allowing Claude, GPT, and other LLM-based agents to request web scraping without direct API knowledge. Supports tool parameters for URL, selector, wait conditions, and proxy configuration, with response serialization to JSON for LLM consumption.","intents":["Enable AI agents to scrape web content as part of multi-step reasoning workflows","Provide LLMs with web scraping capabilities without requiring direct API integration","Allow agents to dynamically choose between HTTP, browser, and stealth fetching based on target site characteristics"],"best_for":["AI researchers building agentic systems that require web scraping capabilities","Teams integrating Scrapling with Claude, GPT, or other LLM-based agents","Developers building AI assistants that need to fetch and analyze web content"],"limitations":["MCP server adds ~200-500ms latency per tool call due to serialization and network overhead","LLM-generated selectors are often incorrect; requires fallback to human-provided selectors","Tool schema complexity may confuse LLMs; requires careful prompt engineering","No streaming response support; large HTML responses must be buffered and serialized","Security: exposing scraping as MCP tool requires strict URL allowlisting to prevent abuse"],"requires":["MCP server implementation (server.json configuration)","LLM client with MCP tool-calling support (Claude, GPT-4, etc.)","Network connectivity between LLM client and MCP server"],"input_types":["Tool call requests with URL, selector, and configuration parameters","LLM-generated CSS/XPath selectors","Wait condition specifications"],"output_types":["JSON-serialized HTML content","Extracted text and attribute values","Tool call status and error messages"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__cap_9","uri":"capability://automation.workflow.concurrent.and.asynchronous.request.execution.with.asyncfetcher","name":"concurrent and asynchronous request execution with asyncfetcher","description":"AsyncFetcher and AsyncSession provide async/await interfaces for concurrent request execution using Python's asyncio. Supports concurrent HTTP requests via httpx.AsyncClient and concurrent browser operations via Playwright's async APIs. Enables batching of multiple requests with configurable concurrency limits to avoid overwhelming target servers or exhausting local resources. Integrates with Spider framework for concurrent crawl execution with automatic request queuing and deduplication across async tasks.","intents":["Execute multiple scraping requests concurrently to improve throughput","Implement rate-limiting and concurrency control to avoid overwhelming target servers","Build async-native crawlers that integrate with async Python frameworks (FastAPI, aiohttp)"],"best_for":["Data teams building high-throughput crawlers with thousands of URLs","Developers integrating Scrapling into async Python applications (FastAPI, Starlette)","Teams requiring fine-grained concurrency control and rate-limiting"],"limitations":["Async browser operations are slower than sync due to event loop overhead; ~10-20% throughput penalty","Concurrency limits must be tuned per-target; too high causes rate-limiting, too low wastes resources","Async exception handling is complex; errors in concurrent tasks can silently fail without proper try/except","Memory overhead scales with concurrency; 100 concurrent browser tabs require ~2-4GB RAM","Debugging async code is harder than sync; stack traces are less informative"],"requires":["Python 3.10+ with asyncio support","AsyncFetcher or AsyncSession class","Understanding of async/await patterns and event loop management"],"input_types":["URL lists or async generators","Concurrency limit integers","Async callback functions for request/response processing"],"output_types":["Async iterables of Response objects","Concurrency statistics and throughput metrics","Error lists from failed requests"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-d4vinci-scrapling__headline","uri":"capability://data.processing.analysis.adaptive.web.scraping.framework","name":"adaptive web scraping framework","description":"Scrapling is an adaptive web scraping framework that allows users to handle everything from single HTTP requests to full-scale crawls, integrating advanced features like browser automation and stealth capabilities for anti-bot systems.","intents":["best web scraping framework","web scraping framework for Python","adaptive web scraping tools","open-source web scraping solutions","web scraping frameworks with stealth capabilities"],"best_for":["developers needing flexible scraping solutions","projects requiring stealth against anti-bot measures"],"limitations":[],"requires":["Python 3.10+"],"input_types":["HTML","URLs"],"output_types":["scraped data","structured data"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Playwright library for browser automation (optional until BrowserFetcher is used)","httpx or requests library for static HTTP fetching","BrowserFetcher or StealthyFetcher (static HTTP fetcher cannot detect DOM mutations)","Playwright browser instance with DOM inspection capabilities","CSS and XPath selector knowledge for fallback strategy configuration","Response object from fetcher","Custom converter functions or classes","Target type definitions (dataclass, Pydantic model, etc.)","BrowserConfig class from scrapling.engines"],"failure_modes":["Lazy imports add ~50-100ms to first access of browser-dependent classes","Response interface abstraction may hide fetcher-specific optimizations (e.g., HTTP connection pooling details)","Browser sessions require explicit lifecycle management; no automatic cleanup on exception","Fallback selector strategies add ~100-300ms per element relocation attempt","Text content matching fails on dynamically generated or identical text nodes","Proximity-based recovery unreliable if multiple similar elements exist in DOM","No support for elements that are completely removed and re-rendered with different attributes","Custom converters require boilerplate code; no automatic type inference from HTML structure","Converter chaining adds ~50-100ms per conversion step","Validation errors in converters are not automatically retried; requires explicit error handling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.806878605466671,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-05-02T16:58:57Z"},"community":{"stars":42414,"forks":3851,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-d4vinci-scrapling","compare_url":"https://unfragile.ai/compare?artifact=mcp-d4vinci-scrapling"}},"signature":"fC+9u2lQ2M0Ki504Y9JJeXCbTDCmQpg2DouXM/h7iaVjznybtAOg/t6t9ohguIJKav1f/6Qm7cDOiviOhKiRCw==","signedAt":"2026-06-22T13:27:05.035Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-d4vinci-scrapling","artifact":"https://unfragile.ai/mcp-d4vinci-scrapling","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-d4vinci-scrapling","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}