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The server exposes resources, tools, and prompts as MCP-compliant endpoints that clients can discover and invoke, with built-in support for streaming responses and error handling through the MCP transport layer.","intents":["I want to expose custom threat monitoring tools to Claude so it can analyze security events autonomously","I need to create a standardized interface for LLM-based security analysis that works across different Claude versions","I want to build an MCP server that integrates threat intelligence data with LLM reasoning"],"best_for":["Security teams building LLM-powered threat analysis workflows","Developers integrating threat monitoring into Claude-based agents","Organizations standardizing on MCP for AI tool orchestration"],"limitations":["MCP protocol overhead adds latency for high-frequency tool calls (typical 50-200ms per round-trip)","No built-in authentication/authorization — relies on transport layer security (TLS, API keys)","Streaming responses require client-side buffering for large threat datasets","Limited to request/response patterns — no true bidirectional push notifications from server to client"],"requires":["MCP client implementation (Claude desktop, Cline, or custom MCP client)","Node.js 16+ or Python 3.8+ (depending on implementation language)","Network connectivity between MCP client and server","Understanding of JSON-RPC 2.0 protocol specification"],"input_types":["JSON-RPC method calls with parameters","Tool invocation requests with arguments","Resource URIs for data retrieval"],"output_types":["JSON-structured tool results","Streaming text responses","Resource content (text, structured data)","Error responses with diagnostic information"],"categories":["tool-use-integration","mcp-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amin-azmoodehh-sentineltm__cap_1","uri":"capability://tool.use.integration.threat.intelligence.resource.exposure.via.mcp.resources","name":"threat intelligence resource exposure via mcp resources","description":"Exposes threat monitoring data, alerts, and security intelligence as MCP resources that clients can discover and retrieve. Resources are identified by URIs and can return structured threat data (alerts, indicators, events) in formats optimized for LLM processing, with support for filtering, pagination, and real-time updates through the MCP resource subscription mechanism.","intents":["I want Claude to access current threat alerts and security events without making external API calls","I need to expose historical threat data and indicators of compromise to an LLM for pattern analysis","I want to provide real-time threat intelligence feeds to Claude through a standardized interface"],"best_for":["Security operations centers (SOCs) integrating LLM analysis into incident response","Threat intelligence teams automating alert triage and correlation","DevSecOps teams building AI-assisted security monitoring"],"limitations":["Resource discovery is static at server startup — dynamic threat data requires polling or subscription patterns","Large threat datasets may exceed context window limits when retrieved as single resources","No built-in caching — repeated resource requests hit the backend each time","Resource URIs must be pre-defined; ad-hoc threat queries require tool-based access instead"],"requires":["MCP client with resource discovery support","Backend threat data source (SIEM, threat feed, monitoring platform)","Proper URI schema design for threat resource organization"],"input_types":["Resource URI requests","Optional filter/query parameters in URI"],"output_types":["JSON threat alerts and events","Structured indicator data (IPs, domains, hashes)","Markdown-formatted threat summaries","Raw log data or STIX/MISP formatted intelligence"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amin-azmoodehh-sentineltm__cap_2","uri":"capability://tool.use.integration.threat.analysis.and.response.tools.via.mcp.tool.registry","name":"threat analysis and response tools via mcp tool registry","description":"Registers security-specific tools (threat detection, alert analysis, incident response actions) in the MCP tool registry with JSON schemas that define parameters, return types, and execution semantics. Claude can discover these tools, understand their capabilities through schema inspection, and invoke them with structured arguments, receiving results that feed back into the LLM reasoning loop for iterative threat analysis.","intents":["I want Claude to autonomously analyze security alerts and recommend remediation steps","I need Claude to execute threat hunting queries and correlate results across multiple data sources","I want to enable Claude to trigger incident response workflows based on threat analysis"],"best_for":["Security analysts building AI-assisted threat hunting workflows","Incident response teams automating alert investigation and triage","Threat researchers using LLMs to analyze and correlate threat data"],"limitations":["Tool execution is synchronous — long-running threat analysis operations block the LLM response","Schema complexity can exceed LLM understanding, requiring careful tool design and descriptions","No built-in transaction support — failed tool calls may leave threat data in inconsistent state","Tool result size is bounded by context window, limiting analysis of large threat datasets in single call"],"requires":["MCP client supporting tool calling (Claude, Cline, or custom implementation)","Backend threat analysis engines or APIs to execute actual security operations","Well-designed JSON schemas for threat tools with clear parameter documentation"],"input_types":["Tool invocation with structured parameters (alert IDs, IP addresses, domains, time ranges)","Optional context from previous tool calls"],"output_types":["Threat analysis results (severity, indicators, attack patterns)","Remediation recommendations","Incident response action confirmations","Structured threat correlation data"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amin-azmoodehh-sentineltm__cap_3","uri":"capability://planning.reasoning.prompt.templates.for.threat.analysis.workflows","name":"prompt templates for threat analysis workflows","description":"Exposes reusable prompt templates through the MCP prompts mechanism that guide Claude through structured threat analysis workflows. Templates can include system instructions for threat assessment, few-shot examples of alert analysis, and workflow scaffolding that ensures consistent threat evaluation methodology across multiple analysis sessions.","intents":["I want Claude to follow a consistent threat assessment methodology across all alert analyses","I need to provide domain-specific threat analysis examples that improve Claude's reasoning quality","I want to enforce security best practices in how Claude approaches threat investigation"],"best_for":["Security teams standardizing threat analysis procedures across analysts","Organizations training LLMs on proprietary threat assessment methodologies","Teams building repeatable threat hunting and incident response workflows"],"limitations":["Prompt templates are static — dynamic threat context must be injected at invocation time","Template versioning requires manual management; no built-in version control or rollback","Large prompt templates consume significant token budget, impacting cost and latency","Template effectiveness depends on Claude's instruction-following capability, which varies by model version"],"requires":["MCP client supporting prompt discovery and invocation","Well-designed threat analysis templates with clear instructions and examples","Understanding of prompt engineering best practices for security domain"],"input_types":["Prompt template selection","Optional parameters to customize template behavior (threat type, severity level, etc.)"],"output_types":["Structured threat analysis following template methodology","Recommendations and remediation steps","Confidence scores and reasoning chains"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amin-azmoodehh-sentineltm__cap_4","uri":"capability://tool.use.integration.real.time.threat.event.streaming.and.subscription","name":"real-time threat event streaming and subscription","description":"Implements MCP subscription/streaming mechanisms to push threat events, alerts, and security updates from the server to connected Claude clients in real-time. Uses server-sent events (SSE) or WebSocket-based streaming to deliver threat data as it occurs, enabling Claude to react to emerging threats without polling or waiting for explicit resource requests.","intents":["I want Claude to monitor threat feeds and alert me immediately when critical threats are detected","I need Claude to correlate real-time security events and identify attack patterns as they unfold","I want to enable continuous threat monitoring without repeatedly querying the server"],"best_for":["Security operations centers (SOCs) with continuous threat monitoring requirements","Organizations needing real-time threat correlation and response","Teams building AI-powered security dashboards with live threat updates"],"limitations":["Streaming adds complexity to MCP client implementation — not all clients support subscriptions","High-volume threat streams can overwhelm Claude's processing capacity, requiring filtering/sampling","Network interruptions can cause event loss — requires client-side buffering and reconnection logic","Streaming state is not persisted — server restart loses in-flight threat events"],"requires":["MCP client with streaming/subscription support (custom implementation required for most clients)","Persistent connection between client and server (WebSocket or SSE)","Threat event source with real-time data availability","Proper error handling and reconnection logic in client"],"input_types":["Subscription requests with optional filters (threat type, severity, source)","Acknowledgment messages for received events"],"output_types":["Real-time threat alert streams","Security event notifications","Threat correlation updates","Incident escalation notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amin-azmoodehh-sentineltm__cap_5","uri":"capability://data.processing.analysis.multi.source.threat.data.aggregation.and.normalization","name":"multi-source threat data aggregation and normalization","description":"Aggregates threat data from multiple backend sources (SIEM, threat feeds, monitoring platforms, APIs) and normalizes it into a unified format that the MCP server exposes to Claude. Handles schema translation, data enrichment, and format conversion so Claude receives consistent threat intelligence regardless of source, with built-in deduplication and correlation logic.","intents":["I want Claude to analyze threats from multiple security tools without worrying about format differences","I need to correlate alerts from SIEM, EDR, and threat feeds into a single coherent threat picture","I want to enrich raw threat data with context (asset info, threat intelligence) before exposing it to Claude"],"best_for":["Organizations with heterogeneous security tool stacks (multiple SIEMs, EDRs, threat feeds)","Security teams needing unified threat intelligence across multiple data sources","Teams building centralized threat analysis platforms with LLM integration"],"limitations":["Data aggregation adds latency — normalized threat data may lag behind source systems by seconds to minutes","Schema translation can lose source-specific context or metadata not present in normalized format","Deduplication logic is heuristic-based and may miss correlated threats or create false correlations","Enrichment requires additional API calls, increasing operational complexity and cost"],"requires":["Connectors/adapters for each threat data source (SIEM API, threat feed integration, etc.)","Unified threat data schema design","Backend data aggregation and normalization pipeline","Deduplication and correlation logic"],"input_types":["Raw threat data from multiple sources (alerts, events, indicators)","Enrichment data (asset information, threat intelligence, context)"],"output_types":["Normalized threat alerts","Correlated threat events","Enriched threat intelligence","Deduplicated alert streams"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amin-azmoodehh-sentineltm__cap_6","uri":"capability://memory.knowledge.threat.context.injection.into.llm.conversation.state","name":"threat context injection into llm conversation state","description":"Maintains conversation state within the MCP server that includes relevant threat context, previous analysis results, and incident history. Automatically injects this context into Claude's conversation history so the LLM can reason about threat patterns across multiple interactions without requiring explicit context passing, using MCP's context management capabilities.","intents":["I want Claude to remember threat analysis context across multiple conversation turns","I need Claude to correlate current alerts with historical incidents and previous analysis","I want to maintain incident investigation state so Claude can pick up where it left off"],"best_for":["Security analysts conducting multi-turn threat investigations","Incident response teams tracking investigation progress across sessions","Teams building persistent threat analysis workflows"],"limitations":["Context state grows with conversation length, eventually exceeding token limits","No built-in persistence — context is lost if server restarts or connection drops","Context injection adds overhead to each LLM call, increasing latency and token usage","Stale context can mislead threat analysis if threat landscape changes between conversation turns"],"requires":["MCP client supporting context management","Server-side conversation state storage (in-memory or persistent database)","Mechanism to serialize and deserialize threat context"],"input_types":["Threat analysis requests with implicit context reference","Context update messages from Claude"],"output_types":["Threat analysis with historical context incorporated","Updated conversation state","Context summary for debugging"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude desktop, Cline, or custom MCP client)","Node.js 16+ or Python 3.8+ (depending on implementation language)","Network connectivity between MCP client and server","Understanding of JSON-RPC 2.0 protocol specification","MCP client with resource discovery support","Backend threat data source (SIEM, threat feed, monitoring platform)","Proper URI schema design for threat resource organization","MCP client supporting tool calling (Claude, Cline, or custom implementation)","Backend threat analysis engines or APIs to execute actual security operations","Well-designed JSON schemas for threat tools with clear parameter documentation"],"failure_modes":["MCP protocol overhead adds latency for high-frequency tool calls (typical 50-200ms per round-trip)","No built-in authentication/authorization — relies on transport layer security (TLS, API keys)","Streaming responses require client-side buffering for large threat datasets","Limited to request/response patterns — no true bidirectional push notifications from server to client","Resource discovery is static at server startup — dynamic threat data requires polling or subscription patterns","Large threat datasets may exceed context window limits when retrieved as single resources","No built-in caching — repeated resource requests hit the backend each time","Resource URIs must be pre-defined; ad-hoc threat queries require tool-based access instead","Tool execution is synchronous — long-running threat analysis operations block the LLM response","Schema complexity can exceed LLM understanding, requiring careful tool design and descriptions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:25.720Z","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=amin-azmoodehh-sentineltm","compare_url":"https://unfragile.ai/compare?artifact=amin-azmoodehh-sentineltm"}},"signature":"cjUJxfykhyKAL1awXZZw3MIwZsX43bduOKd6mJvIwQGJUMiJoS1FHWlABoE2zzqzuHqLS/krDH/MEqIqRzPYBQ==","signedAt":"2026-06-20T00:44:35.814Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/amin-azmoodehh-sentineltm","artifact":"https://unfragile.ai/amin-azmoodehh-sentineltm","verify":"https://unfragile.ai/api/v1/verify?slug=amin-azmoodehh-sentineltm","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"}}