{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-firecrawl","slug":"firecrawl","name":"Firecrawl","type":"mcp","url":"https://github.com/mendableai/firecrawl-mcp-server","page_url":"https://unfragile.ai/firecrawl","categories":["mcp-servers","data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-firecrawl__cap_0","uri":"capability://tool.use.integration.mcp.based.web.scraping.with.llm.aware.extraction","name":"mcp-based web scraping with llm-aware extraction","description":"Exposes Firecrawl's web scraping API through the Model Context Protocol (MCP), allowing LLM agents and tools to directly invoke web data extraction without custom HTTP client code. The MCP server translates tool-use requests into Firecrawl API calls, handling authentication, response marshaling, and error propagation back to the LLM runtime. This enables seamless integration into agentic workflows where web data fetching is a discrete step in multi-tool reasoning chains.","intents":["I want my Claude/LLM agent to fetch and parse web content as part of a reasoning chain without writing custom API integration code","I need to expose web scraping capabilities to an LLM through a standardized tool interface that works across different LLM providers","I'm building an agent that needs to research web content dynamically during task execution"],"best_for":["AI agent developers building multi-tool reasoning systems with Claude or other MCP-compatible LLMs","Teams integrating web data extraction into LLM-powered workflows","Developers prototyping agents that need real-time web access without custom integrations"],"limitations":["Depends on Firecrawl API availability and rate limits — no local fallback for scraping","MCP protocol overhead adds latency compared to direct HTTP calls (~50-200ms per request)","Requires valid Firecrawl API key; no built-in caching of scraped content across requests","Limited to whatever extraction modes Firecrawl supports (markdown, structured data, etc.)"],"requires":["Firecrawl API key (from firecrawl.dev)","MCP-compatible LLM client (Claude, or other MCP-supporting runtime)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation)","Network access to Firecrawl API endpoints"],"input_types":["URL string","extraction mode specification (markdown, structured, screenshot, etc.)","optional CSS selectors or JSON schema for structured extraction"],"output_types":["markdown-formatted text","structured JSON data","screenshot/image data","raw HTML"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_1","uri":"capability://data.processing.analysis.markdown.formatted.web.content.extraction","name":"markdown-formatted web content extraction","description":"Converts web pages into clean, LLM-friendly markdown format by parsing HTML structure, removing boilerplate (navigation, ads, footers), and preserving semantic hierarchy (headings, lists, links). The extraction uses Firecrawl's backend processing to identify main content blocks and convert them to markdown, making the output suitable for direct ingestion into LLM context windows without additional parsing or cleanup.","intents":["I want to fetch a web page and get clean markdown text that I can feed directly into an LLM prompt","I need to extract article content from a website while removing navigation, ads, and other noise","I'm building a research agent that needs readable, structured text from web sources"],"best_for":["LLM-powered research and summarization agents","Content aggregation pipelines that need clean text input","Developers building RAG systems that index web content"],"limitations":["Markdown conversion quality depends on HTML structure — poorly-formatted pages may produce degraded output","No control over markdown dialect or formatting preferences (e.g., link style, heading levels)","Large pages may be truncated or summarized by Firecrawl to fit API response limits","JavaScript-rendered content requires Firecrawl's JavaScript execution (may incur additional latency/cost)"],"requires":["Firecrawl API key","Valid, publicly-accessible URL","MCP server with markdown extraction mode enabled"],"input_types":["URL string"],"output_types":["markdown-formatted text"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_2","uri":"capability://data.processing.analysis.schema.based.structured.data.extraction.from.web.pages","name":"schema-based structured data extraction from web pages","description":"Extracts data from web pages into a user-defined JSON schema by sending the schema to Firecrawl's backend, which uses LLM-based understanding to locate and extract matching fields from the page content. The MCP server accepts a JSON schema definition and returns extracted data conforming to that schema, enabling type-safe, structured data collection from unstructured web content without manual parsing logic.","intents":["I want to extract specific fields (e.g., product name, price, rating) from a product page and get back structured JSON","I need to scrape data from multiple pages with the same schema and aggregate it into a database","I'm building an agent that needs to extract business information (contact, hours, address) from company websites"],"best_for":["Data extraction pipelines that need structured output (e.g., product catalogs, business directories)","Agents that need to extract specific fields from diverse web sources","Teams building web-to-database workflows without custom parsing"],"limitations":["Extraction accuracy depends on page structure and schema clarity — ambiguous schemas may produce inconsistent results","No validation that extracted data matches schema types (e.g., price as string vs number)","Schema must be defined upfront; no dynamic schema inference from page content","Large or complex schemas may hit Firecrawl API limits on response size"],"requires":["Firecrawl API key","Valid JSON schema definition","URL pointing to page with extractable content matching schema","MCP server with structured extraction mode enabled"],"input_types":["URL string","JSON schema (object with properties, types, descriptions)"],"output_types":["JSON object conforming to provided schema"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_3","uri":"capability://image.visual.screenshot.and.visual.content.capture.from.web.pages","name":"screenshot and visual content capture from web pages","description":"Captures a visual screenshot of a web page (including JavaScript-rendered content) and returns it as an image, enabling agents to analyze page layout, visual design, or extract information from visual elements. The MCP server invokes Firecrawl's screenshot capability, which renders the page in a headless browser and returns the image in a format suitable for vision-capable LLMs or image analysis tools.","intents":["I want to capture a visual screenshot of a web page to analyze its layout or visual design","I need to extract information from visual elements (charts, diagrams, infographics) that aren't easily parsed as text","I'm building an agent that needs to verify page rendering or detect visual changes"],"best_for":["Agents that need visual analysis of web pages (e.g., UI testing, design review)","Vision-capable LLM workflows that analyze page layouts or visual content","Quality assurance and monitoring systems that track visual changes"],"limitations":["Screenshot generation adds significant latency (~2-5 seconds per page) compared to text extraction","Image size and resolution may be limited by Firecrawl API constraints","Dynamic content (animations, hover states) is captured at a single point in time","Vision LLM analysis of screenshots adds additional API calls and cost"],"requires":["Firecrawl API key","Valid, publicly-accessible URL","MCP server with screenshot mode enabled","Optional: vision-capable LLM for analyzing the screenshot"],"input_types":["URL string","optional viewport size specification"],"output_types":["image (PNG or JPEG)","base64-encoded image data"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_4","uri":"capability://data.processing.analysis.batch.web.scraping.with.url.list.processing","name":"batch web scraping with url list processing","description":"Processes multiple URLs in a single request, extracting data from each page using the same extraction mode (markdown, structured, or screenshot). The MCP server batches URLs and sends them to Firecrawl's API, which processes them in parallel or sequentially depending on plan limits, returning results for each URL. This enables efficient bulk data collection from multiple web sources without sequential API calls.","intents":["I want to scrape 50+ product pages and extract structured data from all of them efficiently","I need to monitor multiple competitor websites and extract pricing/content changes","I'm building a research agent that needs to collect data from a list of URLs in one operation"],"best_for":["Bulk data extraction pipelines (product catalogs, competitor monitoring, market research)","Agents that need to process multiple sources in a single task","Teams with large-scale web scraping requirements"],"limitations":["Batch processing is subject to Firecrawl API rate limits and plan quotas — large batches may be queued or rejected","No built-in retry logic for failed URLs — partial failures require manual re-processing","Results are returned as a flat list with no grouping or ordering guarantees","Batch requests may have higher latency than individual requests due to backend queuing"],"requires":["Firecrawl API key with batch processing support","List of valid, publicly-accessible URLs","MCP server with batch mode enabled"],"input_types":["array of URL strings","extraction mode (markdown, structured, screenshot)"],"output_types":["array of extraction results (one per URL)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_5","uri":"capability://data.processing.analysis.javascript.enabled.dynamic.content.rendering.and.extraction","name":"javascript-enabled dynamic content rendering and extraction","description":"Renders web pages with JavaScript execution enabled, allowing extraction of content that is generated dynamically by client-side scripts (e.g., React, Vue, Angular apps). The MCP server passes a flag to Firecrawl's backend, which uses a headless browser to execute JavaScript, wait for content to load, and then extract data. This enables scraping of modern single-page applications and JavaScript-heavy websites that would return empty or incomplete content with static HTML parsing.","intents":["I want to scrape a React/Vue app that loads content dynamically with JavaScript","I need to extract data from a website that uses client-side rendering instead of server-side HTML","I'm building an agent that needs to handle modern web applications with dynamic content"],"best_for":["Agents scraping modern single-page applications (SPAs)","Data extraction from JavaScript-heavy websites","Teams that need to scrape content that isn't available in static HTML"],"limitations":["JavaScript rendering adds significant latency (~3-10 seconds per page) compared to static extraction","Increased cost per request (Firecrawl charges more for JS-enabled scraping)","No control over JavaScript execution timeout or wait conditions — may timeout on slow-loading pages","Cannot interact with page (click buttons, fill forms) — only passive content extraction"],"requires":["Firecrawl API key with JavaScript rendering enabled","Valid URL pointing to JavaScript-rendered content","MCP server with JS rendering mode enabled"],"input_types":["URL string","extraction mode (markdown, structured, screenshot)","optional wait time for content to load"],"output_types":["markdown text, JSON, or image (depending on extraction mode)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_6","uri":"capability://data.processing.analysis.intelligent.content.filtering.and.boilerplate.removal","name":"intelligent content filtering and boilerplate removal","description":"Automatically identifies and removes non-content elements (navigation menus, sidebars, ads, footers, cookie banners) from extracted web pages, isolating the main article or content block. Firecrawl's backend uses heuristics and LLM-based understanding to distinguish main content from boilerplate, returning only the relevant text or structured data. This preprocessing step ensures that extracted content is clean and focused, reducing noise in downstream LLM processing.","intents":["I want to extract article text without navigation, ads, or sidebar content","I need to feed clean web content to an LLM without manual cleanup","I'm building a content aggregation system that needs to remove boilerplate automatically"],"best_for":["Content aggregation and news scraping pipelines","RAG systems that index web content","LLM-powered research and summarization agents"],"limitations":["Boilerplate detection is heuristic-based and may fail on unusual page layouts","No user control over what is considered 'boilerplate' — one-size-fits-all approach","Some legitimate content (sidebars with related articles) may be incorrectly filtered","Content filtering is applied automatically; no option to disable or customize"],"requires":["Firecrawl API key","Valid URL with identifiable main content block"],"input_types":["URL string"],"output_types":["cleaned markdown or structured data"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_7","uri":"capability://memory.knowledge.mcp.resource.based.url.caching.and.metadata.exposure","name":"mcp resource-based url caching and metadata exposure","description":"Exposes scraped web pages as MCP resources, allowing agents to reference previously-fetched content by URL without re-scraping. The MCP server maintains a resource registry of extracted pages (with metadata like extraction time, mode, content hash) and allows agents to query or reference these resources in subsequent tool calls. This reduces redundant API calls and enables efficient content reuse within multi-step agent workflows.","intents":["I want to fetch a web page once and reference it in multiple agent steps without re-scraping","I need to track which pages have been extracted and avoid duplicate requests","I'm building an agent that needs to compare content from the same page extracted at different times"],"best_for":["Multi-step agent workflows that reference the same web content multiple times","Agents that need to track extraction history and metadata","Systems with limited API quotas that benefit from caching"],"limitations":["Cache is in-memory or local to the MCP server — no persistence across server restarts","No built-in cache invalidation or TTL — stale content may be served if pages are updated","Cache size is limited by available memory — large-scale caching requires external storage","No distributed caching — each MCP server instance has its own cache"],"requires":["Firecrawl API key","MCP server with resource caching enabled"],"input_types":["URL string (for cache lookup)"],"output_types":["cached extraction result with metadata (extraction time, mode, content hash)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-firecrawl__cap_8","uri":"capability://automation.workflow.error.handling.and.fallback.strategies","name":"error handling and fallback strategies","description":"Implements robust error handling for failed requests, timeouts, and invalid URLs, with configurable fallback behaviors (retry, partial extraction, error reporting). The MCP server catches Firecrawl API errors and returns structured error information to the LLM client for decision-making.","intents":["I want my agent to gracefully handle scraping failures without crashing","I need to distinguish between temporary failures (retry) and permanent errors (skip)","I want detailed error information to debug scraping issues"],"best_for":["Production agent deployments requiring reliability","Batch scraping workflows with many URLs","Debugging and monitoring scraping operations"],"limitations":["Retry logic adds latency for failed requests","No automatic fallback to alternative extraction methods","Error messages depend on Firecrawl API response quality","Cannot recover from authentication or access denied errors"],"requires":["Firecrawl API key","MCP server with error handling middleware","Client-side timeout and retry configuration"],"input_types":["URL string","Optional retry and timeout parameters"],"output_types":["Structured error object with code, message, and retry suggestion","Partial extraction results if available","Metadata about failure (timeout, access denied, invalid URL)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"moderate","permissions":["Firecrawl API key (from firecrawl.dev)","MCP-compatible LLM client (Claude, or other MCP-supporting runtime)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation)","Network access to Firecrawl API endpoints","Firecrawl API key","Valid, publicly-accessible URL","MCP server with markdown extraction mode enabled","Valid JSON schema definition","URL pointing to page with extractable content matching schema","MCP server with structured extraction mode enabled"],"failure_modes":["Depends on Firecrawl API availability and rate limits — no local fallback for scraping","MCP protocol overhead adds latency compared to direct HTTP calls (~50-200ms per request)","Requires valid Firecrawl API key; no built-in caching of scraped content across requests","Limited to whatever extraction modes Firecrawl supports (markdown, structured data, etc.)","Markdown conversion quality depends on HTML structure — poorly-formatted pages may produce degraded output","No control over markdown dialect or formatting preferences (e.g., link style, heading levels)","Large pages may be truncated or summarized by Firecrawl to fit API response limits","JavaScript-rendered content requires Firecrawl's JavaScript execution (may incur additional latency/cost)","Extraction accuracy depends on page structure and schema clarity — ambiguous schemas may produce inconsistent results","No validation that extracted data matches schema types (e.g., price as string vs number)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.039Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=firecrawl","compare_url":"https://unfragile.ai/compare?artifact=firecrawl"}},"signature":"eQ+vP26ZxtwiA7zJSMDkHIdIl+G/x2yn02viEkCGLSLR4Je8jWHgwE66Ho3zwz24CPAVTgCwyiopxWH3kTnDDw==","signedAt":"2026-06-22T18:28:47.151Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/firecrawl","artifact":"https://unfragile.ai/firecrawl","verify":"https://unfragile.ai/api/v1/verify?slug=firecrawl","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"}}