{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-agentql","slug":"agentql","name":"AgentQL","type":"mcp","url":"https://github.com/tinyfish-io/agentql-mcp","page_url":"https://unfragile.ai/agentql","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-agentql__cap_0","uri":"capability://data.processing.analysis.dom.to.structured.data.extraction.via.natural.language.queries","name":"dom-to-structured-data extraction via natural language queries","description":"AgentQL translates natural language queries into executable extraction logic that traverses the DOM tree and converts unstructured HTML/CSS into structured JSON. The MCP server acts as a bridge, accepting agent queries and returning parsed data without requiring the agent to write CSS selectors or XPath expressions. This uses a query language abstraction layer that maps semantic intent to DOM traversal patterns.","intents":["Extract product listings with prices and ratings from e-commerce sites without writing CSS selectors","Scrape structured data from dynamic web pages that render after JavaScript execution","Query specific UI elements by their semantic meaning rather than HTML structure","Build web automation workflows that adapt to minor DOM changes without code updates"],"best_for":["AI agents and LLM applications that need to extract web data programmatically","Teams building web scraping pipelines that must handle unstructured or semi-structured HTML","Developers automating data collection from sites where the DOM structure is fragile or frequently changes"],"limitations":["Requires JavaScript rendering for dynamic content — static HTML parsing alone may miss client-rendered data","Query language abstraction adds latency compared to direct CSS selector execution","No built-in caching or persistence — each query re-fetches and re-parses the target page","Limited to web content accessible via standard HTTP/HTTPS; cannot bypass authentication or CORS restrictions without additional setup"],"requires":["MCP-compatible AI agent framework (Claude, custom LLM agent, or similar)","Network access to target websites","AgentQL API credentials or self-hosted AgentQL service","Node.js 16+ or Python 3.8+ for running the MCP server"],"input_types":["natural language query string (e.g., 'get all product names and prices')","URL or page HTML content","optional context about page structure or expected data schema"],"output_types":["JSON structured data matching the query intent","array of objects with extracted fields","null or empty result if no matching elements found"],"categories":["data-processing-analysis","web-scraping","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentql__cap_1","uri":"capability://tool.use.integration.mcp.server.integration.for.agent.tool.calling","name":"mcp server integration for agent tool calling","description":"AgentQL exposes its extraction capabilities as an MCP (Model Context Protocol) server, allowing any MCP-compatible AI agent to invoke web data extraction as a native tool. The server implements the MCP tool-calling interface, translating agent function calls into AgentQL queries and returning results in a format the agent can reason about. This enables seamless integration without custom API client code or webhook orchestration.","intents":["Add web scraping capability to an existing Claude or LLM agent without modifying agent code","Chain web extraction with other MCP tools in a multi-step agent workflow","Enable agents to autonomously gather data from websites as part of decision-making loops","Expose AgentQL as a reusable service that multiple agents can call concurrently"],"best_for":["Developers building multi-tool AI agents that need web data as one capability among many","Teams deploying agents in MCP-compatible environments (Claude Desktop, custom agent frameworks)","Organizations wanting to standardize on MCP for tool orchestration across multiple data sources"],"limitations":["Requires MCP-compatible agent framework — not compatible with REST-only or OpenAI function-calling-only agents","Tool calling overhead adds ~50-200ms per invocation due to serialization and MCP protocol handling","No built-in rate limiting or concurrent request management — agent must implement backpressure","Error handling relies on agent's MCP error handling — no built-in retry logic at the MCP layer"],"requires":["MCP-compatible AI agent framework (Claude with MCP support, custom agent using mcp-sdk, etc.)","AgentQL MCP server running and accessible (local or remote)","Agent framework with tool-calling support enabled"],"input_types":["MCP tool call with function name and parameters (URL, query string)","JSON-serialized query parameters"],"output_types":["JSON response containing extracted data","MCP error response if extraction fails","structured metadata about extraction success/failure"],"categories":["tool-use-integration","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentql__cap_2","uri":"capability://data.processing.analysis.javascript.aware.page.rendering.and.dom.snapshot.capture","name":"javascript-aware page rendering and dom snapshot capture","description":"AgentQL executes JavaScript on target pages before extraction, ensuring that dynamically-rendered content (React, Vue, Angular apps) is available for querying. The system captures a stable DOM snapshot after rendering completes, allowing queries to operate on the final rendered state rather than initial HTML. This involves browser automation under the hood (likely Puppeteer or Playwright) coordinated with the MCP server.","intents":["Extract data from single-page applications that populate content via JavaScript","Wait for dynamic content to load before attempting extraction","Handle pages with lazy-loading or infinite-scroll patterns","Query the final rendered DOM state without manually triggering page interactions"],"best_for":["Agents scraping modern web applications built with React, Vue, or Angular","Teams extracting data from sites with heavy client-side rendering","Workflows where page load time is variable and must be waited for before extraction"],"limitations":["JavaScript execution adds 2-10 seconds per page due to browser startup and rendering time","Memory overhead from running a headless browser instance — not suitable for high-concurrency scenarios without pooling","Cannot execute arbitrary JavaScript for custom page interactions — limited to rendering the initial page state","Some sites block headless browser access via User-Agent detection or JavaScript-based bot detection"],"requires":["Headless browser binary (Chromium/Chrome) available on the system or in container","Sufficient memory for browser process (typically 100-300MB per instance)","Network access to target websites"],"input_types":["URL to fetch and render","optional timeout for JavaScript execution (default likely 5-10 seconds)","optional viewport size for rendering"],"output_types":["rendered HTML/DOM snapshot as JSON-serializable structure","extraction results based on the rendered state"],"categories":["data-processing-analysis","browser-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentql__cap_3","uri":"capability://data.processing.analysis.adaptive.selector.generation.from.semantic.intent","name":"adaptive selector generation from semantic intent","description":"AgentQL's query language compiler translates natural language extraction intent into optimized DOM selectors and traversal logic without exposing CSS selector syntax to the agent. The system learns from page structure to generate selectors that are resilient to minor DOM changes (e.g., class name changes, attribute reordering). This uses heuristic-based selector generation or learned patterns to map semantic concepts to DOM elements.","intents":["Extract data using human-readable queries instead of brittle CSS selectors","Automatically adapt extraction logic when a website's HTML structure changes slightly","Reduce maintenance burden of selector-based scraping by using semantic intent instead","Enable non-technical users to define extraction rules without learning CSS selector syntax"],"best_for":["Teams maintaining long-lived scraping workflows that must tolerate website redesigns","Agents that need to extract from multiple similar sites with different DOM structures","Organizations where non-developers need to define extraction rules"],"limitations":["Selector generation is heuristic-based and may fail on highly unusual or obfuscated DOM structures","No explicit feedback loop for agents to correct failed extractions — requires re-querying with refined intent","Semantic understanding is limited to common web patterns (product listings, forms, tables) — custom data structures may not be recognized","Selector resilience is not guaranteed — major DOM restructuring will still break extraction"],"requires":["AgentQL service with selector generation model/heuristics","Natural language query describing the data to extract"],"input_types":["natural language query string (e.g., 'all product cards with name, price, and rating')","optional schema hint describing expected output structure"],"output_types":["generated selector(s) or traversal logic (internal representation)","extracted data matching the semantic intent"],"categories":["data-processing-analysis","natural-language-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentql__cap_4","uri":"capability://automation.workflow.concurrent.multi.page.extraction.with.request.batching","name":"concurrent multi-page extraction with request batching","description":"AgentQL MCP server can handle multiple extraction requests concurrently, batching them efficiently to avoid overwhelming target websites or exhausting local browser resources. The server manages a pool of browser instances or request queues, distributing work across available capacity. This enables agents to extract from multiple pages in parallel without blocking on individual page loads or rendering.","intents":["Extract data from 10+ pages in parallel without sequential blocking","Batch multiple extraction queries to the same domain to reuse browser connections","Implement rate limiting and backpressure to avoid overwhelming target websites","Scale agent workflows that require high-volume data extraction"],"best_for":["Agents performing bulk data collection from multiple pages or sites","High-throughput scraping workflows where latency is critical","Teams deploying AgentQL in production with multiple concurrent agents"],"limitations":["Concurrent browser instances consume significant memory — practical limit is typically 5-20 instances per server","No built-in distributed execution — all concurrency is within a single server process","Rate limiting must be configured per-domain to avoid IP blocking or 429 responses","Browser pool management adds complexity — connection leaks or zombie processes can degrade performance"],"requires":["Sufficient system memory for multiple browser instances (500MB-2GB depending on concurrency target)","AgentQL server configured with connection pooling and concurrency limits","Agent framework capable of issuing concurrent tool calls"],"input_types":["multiple extraction requests (URLs and queries) issued concurrently","optional concurrency limit and rate limit parameters"],"output_types":["array of extraction results in the order requests complete","per-request status indicating success or failure"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentql__cap_5","uri":"capability://automation.workflow.error.recovery.and.fallback.extraction.strategies","name":"error recovery and fallback extraction strategies","description":"When a primary extraction query fails (due to page structure changes, timeouts, or rendering issues), AgentQL can attempt fallback strategies such as retrying with a modified query, using alternative selectors, or returning partial results. The MCP server communicates extraction success/failure and partial results to the agent, allowing it to decide whether to retry, refine the query, or proceed with incomplete data.","intents":["Gracefully handle extraction failures without crashing the agent workflow","Retry failed extractions with adjusted parameters or alternative queries","Return partial results when some fields extract successfully but others fail","Provide detailed error information so agents can adapt their strategy"],"best_for":["Long-running agent workflows that must tolerate transient failures","Scraping scenarios where pages are frequently updated or have variable structure","Teams that need visibility into extraction failures for debugging and monitoring"],"limitations":["Fallback strategies are limited to query-level retries — cannot recover from network failures or page unavailability","No automatic learning from failures — each retry uses the same or manually-adjusted query","Partial results may be ambiguous (unclear which fields succeeded vs. failed) without explicit metadata","Retry logic adds latency — failed extractions may take 2-3x longer than successful ones"],"requires":["AgentQL service with fallback strategy configuration","Agent framework capable of handling partial results and error responses"],"input_types":["extraction request with optional retry/fallback configuration","optional timeout and retry count parameters"],"output_types":["extraction result with success/failure status","partial results if some fields extracted successfully","detailed error message indicating what failed and why"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible AI agent framework (Claude, custom LLM agent, or similar)","Network access to target websites","AgentQL API credentials or self-hosted AgentQL service","Node.js 16+ or Python 3.8+ for running the MCP server","MCP-compatible AI agent framework (Claude with MCP support, custom agent using mcp-sdk, etc.)","AgentQL MCP server running and accessible (local or remote)","Agent framework with tool-calling support enabled","Headless browser binary (Chromium/Chrome) available on the system or in container","Sufficient memory for browser process (typically 100-300MB per instance)","AgentQL service with selector generation model/heuristics"],"failure_modes":["Requires JavaScript rendering for dynamic content — static HTML parsing alone may miss client-rendered data","Query language abstraction adds latency compared to direct CSS selector execution","No built-in caching or persistence — each query re-fetches and re-parses the target page","Limited to web content accessible via standard HTTP/HTTPS; cannot bypass authentication or CORS restrictions without additional setup","Requires MCP-compatible agent framework — not compatible with REST-only or OpenAI function-calling-only agents","Tool calling overhead adds ~50-200ms per invocation due to serialization and MCP protocol handling","No built-in rate limiting or concurrent request management — agent must implement backpressure","Error handling relies on agent's MCP error handling — no built-in retry logic at the MCP layer","JavaScript execution adds 2-10 seconds per page due to browser startup and rendering time","Memory overhead from running a headless browser instance — not suitable for high-concurrency scenarios without pooling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:02.370Z","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=agentql","compare_url":"https://unfragile.ai/compare?artifact=agentql"}},"signature":"vS/U32KvxLioxFwAKfkrMyLHa474uQSnHjIhJ0amQSb2rEHPlljlcBMDoDa5DlhI6hvFwSA0KySzCFImmYnZDw==","signedAt":"2026-06-20T00:11:29.418Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentql","artifact":"https://unfragile.ai/agentql","verify":"https://unfragile.ai/api/v1/verify?slug=agentql","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"}}