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Requests are routed through MCP's standardized tool-calling mechanism, enabling asynchronous threat detection with structured JSON responses containing risk indicators, classification, and metadata.","intents":["I want my LLM agent to automatically check if user-provided URLs are safe before processing them","I need to integrate URL threat detection into my Claude/LLM workflow without managing API credentials directly","I want to scan URLs for phishing, malware, and other threats as part of an automated security pipeline","I need to get detailed threat intelligence on URLs including classification and risk scores from within my AI application"],"best_for":["AI application developers building security-aware agents","teams integrating threat detection into LLM-powered workflows","security-focused builders using Claude or other MCP-compatible models","developers building content moderation systems with URL validation"],"limitations":["Depends on urlDNA API availability and rate limits — no local fallback for offline scanning","MCP protocol adds request/response serialization overhead (~50-200ms per scan)","No caching layer built into the MCP server — repeated scans of identical URLs hit the API each time","Requires active internet connection to reach urlDNA backend — cannot function in air-gapped environments","Limited to urlDNA's threat database — cannot integrate with alternative threat intelligence providers without forking"],"requires":["MCP-compatible client (Claude, LLM framework with MCP support)","urlDNA API credentials or account","Network connectivity to urlDNA.io backend","Python 3.8+ or Node.js 16+ (depending on MCP server implementation)"],"input_types":["URL string (http/https)","domain name","batch URL list (if supported by server implementation)"],"output_types":["JSON structured threat report","risk classification (safe/suspicious/malicious)","threat indicators and metadata","confidence scores"],"categories":["tool-use-integration","safety-moderation","security-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-urldna__cap_1","uri":"capability://safety.moderation.threat.classification.and.risk.scoring","name":"threat-classification-and-risk-scoring","description":"Analyzes scanned URLs and returns structured threat classifications (safe, suspicious, malicious) along with confidence scores and risk indicators. The urlDNA backend applies machine learning models and heuristic analysis to categorize URLs based on patterns including domain reputation, SSL certificate validity, content analysis, and known threat databases. Results are returned as JSON objects containing classification labels, numerical risk scores, and detailed threat metadata that can be consumed by downstream LLM reasoning or automated decision-making systems.","intents":["I want to know if a URL is safe, suspicious, or malicious with a confidence score","I need to make automated decisions about whether to allow or block a URL based on threat classification","I want detailed threat indicators (phishing, malware, spam) to understand why a URL is flagged","I need to log and report threat classifications for security auditing and compliance"],"best_for":["security teams building automated threat response systems","LLM application developers implementing content safety guardrails","compliance-focused organizations requiring threat audit trails","developers building user-facing safety warnings and URL validation"],"limitations":["Classification accuracy depends on urlDNA's threat database freshness — zero-day threats may not be detected","No real-time threat intelligence updates — classification reflects database state at scan time","Risk scoring is opaque (black-box model) — no visibility into which specific factors contributed to a score","Cannot customize classification thresholds or weights — fixed urlDNA algorithm","Legitimate URLs may be misclassified if they share infrastructure with malicious sites"],"requires":["urlDNA API access with threat classification endpoint enabled","MCP server implementation with response parsing","Understanding of threat classification schema (safe/suspicious/malicious categories)"],"input_types":["URL string","domain name"],"output_types":["threat classification enum (safe/suspicious/malicious)","risk score (numeric 0-100 or similar scale)","threat indicators array (phishing, malware, spam, etc.)","confidence score","metadata object"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-urldna__cap_2","uri":"capability://tool.use.integration.mcp.tool.registration.and.invocation","name":"mcp-tool-registration-and-invocation","description":"Registers URL scanning as a callable tool within the MCP protocol, allowing LLM clients (Claude, etc.) to discover and invoke URL analysis through standardized tool-calling mechanisms. The MCP server exposes a tool schema defining input parameters (URL), output structure (threat report), and metadata, enabling the LLM to autonomously decide when to scan URLs based on context. Tool invocation is handled through MCP's request/response protocol, with the server translating tool calls into urlDNA API requests and marshaling responses back to the client.","intents":["I want my LLM to automatically detect when a URL needs scanning and invoke the analysis without explicit user instruction","I need the LLM to understand what the URL scanning tool does and when to use it in a multi-step workflow","I want to expose URL scanning as a discoverable capability that the LLM can reason about and apply","I need standardized tool calling that works across different MCP-compatible LLM clients"],"best_for":["LLM application developers building agentic workflows","teams using Claude or other MCP-compatible models","developers implementing multi-tool agent systems","builders standardizing on MCP for tool integration"],"limitations":["Tool discovery and invocation adds latency — LLM must reason about tool availability before calling","MCP protocol overhead (~100-300ms per tool call) vs direct function invocation","Tool schema must be manually defined and kept in sync with urlDNA API changes","No built-in retry logic or circuit breaker — tool failures propagate directly to LLM","Limited to MCP-compatible clients — cannot be used with non-MCP LLM APIs"],"requires":["MCP server implementation (Python or Node.js)","MCP-compatible LLM client (Claude, etc.)","Tool schema definition matching urlDNA API contract","MCP protocol version 1.0+ support"],"input_types":["tool schema definition (JSON)","tool invocation request (MCP protocol)"],"output_types":["tool discovery response (schema + metadata)","tool execution result (JSON threat report)","error responses (MCP error protocol)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-urldna__cap_3","uri":"capability://automation.workflow.batch.url.analysis.orchestration","name":"batch-url-analysis-orchestration","description":"Processes multiple URLs in sequence or parallel through the MCP interface, coordinating individual URL scans and aggregating threat reports into a consolidated analysis. The implementation likely queues URL scan requests, manages API rate limits, and collects results into a structured batch report. This enables workflows where an LLM agent needs to validate multiple URLs (e.g., from a document, email, or user input) and make decisions based on aggregate threat levels across the batch.","intents":["I want to scan multiple URLs from a document or list and get a summary of threats","I need to validate all URLs in user input before processing them further","I want to identify the most dangerous URLs in a batch for prioritized action","I need to generate a threat report covering multiple URLs for compliance or security review"],"best_for":["security analysts processing bulk URL lists","content moderation systems validating user-submitted links","email security systems scanning URLs in messages","web crawlers and link validators"],"limitations":["Rate limiting — urlDNA API likely has per-minute request limits, requiring queue management","Latency scales linearly with batch size — no parallel scanning unless explicitly implemented","Memory overhead for large batches — aggregating results for thousands of URLs may exceed available memory","No built-in deduplication — scanning the same URL multiple times wastes API quota","Timeout risk — very large batches may exceed LLM context window or request timeout limits"],"requires":["MCP server with batch processing logic","urlDNA API with sufficient rate limit quota","LLM context window large enough for batch results","Rate limiting or queue management implementation"],"input_types":["URL array/list","batch configuration (parallel/sequential, timeout)"],"output_types":["batch threat report (aggregated JSON)","per-URL threat classifications","summary statistics (safe count, suspicious count, malicious count)","prioritized threat list"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude, LLM framework with MCP support)","urlDNA API credentials or account","Network connectivity to urlDNA.io backend","Python 3.8+ or Node.js 16+ (depending on MCP server implementation)","urlDNA API access with threat classification endpoint enabled","MCP server implementation with response parsing","Understanding of threat classification schema (safe/suspicious/malicious categories)","MCP server implementation (Python or Node.js)","MCP-compatible LLM client (Claude, etc.)","Tool schema definition matching urlDNA API contract"],"failure_modes":["Depends on urlDNA API availability and rate limits — no local fallback for offline scanning","MCP protocol adds request/response serialization overhead (~50-200ms per scan)","No caching layer built into the MCP server — repeated scans of identical URLs hit the API each time","Requires active internet connection to reach urlDNA backend — cannot function in air-gapped environments","Limited to urlDNA's threat database — cannot integrate with alternative threat intelligence providers without forking","Classification accuracy depends on urlDNA's threat database freshness — zero-day threats may not be detected","No real-time threat intelligence updates — classification reflects database state at scan time","Risk scoring is opaque (black-box model) — no visibility into which specific factors contributed to a score","Cannot customize classification thresholds or weights — fixed urlDNA algorithm","Legitimate URLs may be misclassified if they share infrastructure with malicious sites","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.18,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"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":"2026-06-17T09:51:04.050Z","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=urldna","compare_url":"https://unfragile.ai/compare?artifact=urldna"}},"signature":"QMEu/jP+N+OBqs91BHd8zJnQyuj86BzrztmEmfqrJ83zYnd1ao5jqRn7ZyWJvX5mPhXsZ8emHIYLwSqE7sTSAA==","signedAt":"2026-06-22T09:54:07.968Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/urldna","artifact":"https://unfragile.ai/urldna","verify":"https://unfragile.ai/api/v1/verify?slug=urldna","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"}}