{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_apollion69-vmware-aria-logs","slug":"apollion69-vmware-aria-logs","name":"vmware-aria-logs","type":"mcp","url":"https://github.com/apollion69/vmware-aria-logs","page_url":"https://unfragile.ai/apollion69-vmware-aria-logs","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:apollion69/vmware-aria-logs"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_apollion69-vmware-aria-logs__cap_0","uri":"capability://search.retrieval.vmware.aria.logs.search.with.kql.query.translation","name":"vmware aria logs search with kql query translation","description":"Translates natural language or structured queries into VMware Aria's Kibana Query Language (KQL) and executes searches against the Aria Logs API endpoint. Handles field mapping, operator translation, and result pagination through the MCP protocol, returning structured log events with metadata (timestamp, source, severity, message content).","intents":["Search logs across distributed infrastructure without learning KQL syntax","Retrieve specific log events by application, host, or error pattern for incident investigation","Programmatically query logs from an LLM agent without direct API access"],"best_for":["DevOps teams integrating log search into LLM-powered incident response workflows","SREs building autonomous monitoring agents that need to correlate logs with metrics","Organizations standardizing on Claude/LLM interfaces for operational queries"],"limitations":["Query translation layer may not support all advanced KQL features (regex, complex boolean logic); falls back to simple field matching","Result pagination limited by MCP message size constraints — large result sets require multiple round-trips","No built-in query optimization or cost estimation for expensive searches across multi-terabyte log stores"],"requires":["VMware Aria Operations for Logs instance (v8.0+)","API credentials (username/password or API token) with read access to log indices","Network connectivity to Aria Logs API endpoint (typically port 9200 or 443)","MCP client supporting tool calling (Claude, Anthropic SDK, or compatible)"],"input_types":["natural language query (e.g., 'show me errors from the payment service in the last hour')","KQL query string (e.g., 'severity:ERROR AND source:payment-svc')","structured filter object with field/operator/value tuples"],"output_types":["JSON array of log events with fields: timestamp, source, severity, message, raw_event","Metadata: total_hits, query_time_ms, result_count"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apollion69-vmware-aria-logs__cap_1","uri":"capability://data.processing.analysis.mass.incident.detection.via.signature.clustering.stormbreaker.engine","name":"mass incident detection via signature clustering (stormbreaker engine)","description":"Analyzes log events using signature-based clustering to identify patterns across thousands of similar errors or warnings, grouping them by root cause signature rather than individual message text. The Stormbreaker engine extracts variable fields (timestamps, IPs, request IDs) and clusters on invariant message structure, returning aggregated incident summaries with affected resource counts and severity distribution.","intents":["Detect when a single underlying issue is causing thousands of similar log entries across the infrastructure","Reduce alert fatigue by grouping related incidents into actionable clusters","Identify the root cause signature of a mass incident for faster remediation"],"best_for":["Large-scale infrastructure teams (100+ hosts) experiencing log explosion during incidents","NOCs and SRE teams needing automated incident correlation without manual rule creation","Organizations using Aria Logs as their primary incident detection system"],"limitations":["Clustering accuracy depends on log message structure consistency — unstructured or highly variable logs may produce false negatives","Stormbreaker engine processes logs in-memory; clustering large datasets (>1M events) may timeout or consume significant memory","No machine learning — uses deterministic signature matching, so novel error patterns may not cluster until manually added to signature library","Requires pre-indexed logs in Aria; cannot cluster raw syslog or unstructured text streams"],"requires":["VMware Aria Operations for Logs instance with indexed log data","Logs must follow semi-structured format (key=value, JSON, or syslog with consistent fields)","Minimum 100 log events to produce meaningful clusters","MCP client with tool calling support"],"input_types":["log event array from Aria Logs search (output of search capability)","optional: custom signature patterns (regex or field extraction rules)"],"output_types":["JSON array of incident clusters with fields: signature, count, affected_hosts, affected_services, severity_distribution, first_seen, last_seen","Metadata: total_incidents_detected, clustering_time_ms"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apollion69-vmware-aria-logs__cap_2","uri":"capability://memory.knowledge.vrops.correlation.and.context.enrichment","name":"vrops correlation and context enrichment","description":"Optionally correlates log events with VMware vRealize Operations (vROps) metrics, alerts, and resource topology to enrich incident context. Queries vROps API for related performance metrics, alert history, and resource relationships (e.g., which VMs are running on a host that generated an error log), returning correlated data alongside log search results.","intents":["Understand the full incident context by seeing logs alongside corresponding performance degradation or resource state changes","Correlate application logs with infrastructure metrics to distinguish application vs. infrastructure root causes","Trace an incident across the full stack: logs → metrics → alerts → resource topology"],"best_for":["VMware-centric organizations running both Aria Logs and vROps (integrated monitoring stack)","SRE teams needing full-stack observability without switching between multiple tools","Incident response workflows that require both logs and metrics for root cause analysis"],"limitations":["Requires separate vROps instance and API credentials; adds latency (typically 500ms-2s per correlation query)","Correlation logic is heuristic-based (time window matching, resource name matching) — may produce false correlations if naming conventions are inconsistent","vROps API rate limits may throttle correlation queries during high-volume incident scenarios","Optional feature — if vROps is unavailable, log search still works but without enrichment"],"requires":["VMware vRealize Operations instance (v8.0+) with API enabled","vROps API credentials (read-only access to metrics, alerts, and topology)","Network connectivity to vROps API endpoint","Aria Logs and vROps instances must share common resource identifiers (hostnames, IP addresses, or custom tags)"],"input_types":["log event from Aria Logs search (with source/host/application fields)","time window for metric correlation (default: ±5 minutes from log timestamp)","optional: custom correlation rules (field mappings between Aria and vROps)"],"output_types":["enriched log event with nested vROps context: related_metrics (CPU, memory, disk I/O), related_alerts (severity, timestamp, description), resource_topology (parent resources, dependent resources)","Metadata: correlation_confidence (0-1), correlation_time_ms"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apollion69-vmware-aria-logs__cap_3","uri":"capability://data.processing.analysis.log.event.parsing.and.field.extraction","name":"log event parsing and field extraction","description":"Parses raw log messages to extract structured fields (severity, timestamp, source, application, error code, stack trace) using pattern matching and optional custom parsers. Handles multiple log formats (syslog, JSON, key=value, unstructured text) and normalizes field names to a standard schema, enabling downstream filtering and analysis on extracted fields.","intents":["Extract actionable fields from unstructured log messages for programmatic analysis","Normalize logs from heterogeneous sources (applications, infrastructure, third-party services) into a consistent schema","Enable field-based filtering and aggregation on logs that may not be pre-indexed in Aria"],"best_for":["Teams with mixed log sources (some structured, some unstructured) that need unified parsing","Custom incident detection workflows that require extracted fields not available in Aria's default indices","LLM agents that need to reason about specific log fields (error codes, affected services) rather than raw text"],"limitations":["Parsing accuracy depends on log format consistency; highly variable or malformed logs may fail to parse correctly","Custom parser rules require manual definition; no automatic format detection","Performance degrades with very large log messages (>10KB) or complex regex patterns","Extracted fields are not persisted back to Aria — parsing happens at query time, adding latency"],"requires":["Log events from Aria Logs search or raw log input","Log format specification (syslog, JSON, key=value, or custom regex patterns)","Optional: custom field extraction rules (regex or JSON path expressions)"],"input_types":["raw log message string","log event object from Aria Logs API","array of log events for batch parsing"],"output_types":["parsed log object with normalized fields: severity, timestamp, source, application, error_code, message, stack_trace, custom_fields","Metadata: parse_success (boolean), parse_confidence (0-1), unmatched_fields (array)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apollion69-vmware-aria-logs__cap_4","uri":"capability://planning.reasoning.incident.timeline.reconstruction.and.event.sequencing","name":"incident timeline reconstruction and event sequencing","description":"Reconstructs the chronological sequence of events across multiple log sources and systems to build a coherent incident timeline. Orders events by timestamp, identifies causal relationships (e.g., error in service A triggers timeout in service B), and highlights key turning points (first error, escalation, recovery). Returns a structured timeline with event relationships and severity progression.","intents":["Understand the sequence of events that led to an incident for root cause analysis","Identify the first failure point and trace cascading failures through dependent systems","Communicate incident progression to stakeholders with a clear, chronological narrative"],"best_for":["Post-incident review (PIR) and root cause analysis (RCA) workflows","LLM agents generating incident narratives or automated runbooks","Teams needing to understand failure propagation across microservices or distributed systems"],"limitations":["Causal relationship detection is heuristic-based (time proximity, service dependency) — may miss indirect or delayed causality","Requires accurate timestamps across all systems; clock skew or timezone mismatches can produce incorrect sequencing","Timeline reconstruction is computationally expensive for incidents with >10K events; may timeout on very large incidents","No automatic detection of spurious events (noise, duplicate logs) — may clutter timeline with irrelevant entries"],"requires":["Log events from multiple sources with consistent timestamp format (ISO 8601 or Unix epoch)","Optional: service dependency graph (to infer causal relationships)","Optional: custom event correlation rules (to link related events across services)"],"input_types":["array of log events from Aria Logs search or incident cluster","optional: service dependency topology (JSON graph)","optional: event correlation rules (field matching or regex patterns)"],"output_types":["structured timeline: array of events with fields: timestamp, source, severity, message, event_type (error, warning, recovery), causal_relationships (array of related event IDs), timeline_position (first_error, escalation, recovery, etc.)","Metadata: incident_duration_ms, event_count, critical_path (sequence of events leading to peak severity)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_apollion69-vmware-aria-logs__cap_5","uri":"capability://automation.workflow.log.retention.and.archival.policy.enforcement","name":"log retention and archival policy enforcement","description":"Manages log retention policies and archival workflows within Aria Logs, enforcing data lifecycle rules (e.g., delete logs older than 90 days, archive to cold storage after 30 days). Queries current retention settings, applies policy changes, and reports on archival status and storage utilization, enabling automated compliance and cost optimization.","intents":["Enforce data retention compliance policies (GDPR, HIPAA, SOC 2) without manual intervention","Optimize storage costs by automatically archiving cold logs to cheaper storage tiers","Audit log retention and archival status for compliance reporting"],"best_for":["Compliance-heavy organizations (financial services, healthcare) with strict data retention requirements","Teams managing large log volumes that need cost optimization through tiered storage","Automated compliance workflows that need to enforce retention policies programmatically"],"limitations":["Policy enforcement is one-way (apply policies to Aria) — no rollback or policy versioning","Archival to external storage (S3, Azure Blob) requires separate configuration in Aria; MCP server only triggers archival, doesn't manage destination storage","No fine-grained retention rules (e.g., different retention for different log sources) — policies apply globally or by index","Archival operations are asynchronous; MCP server returns immediately but archival may take hours for large datasets"],"requires":["VMware Aria Operations for Logs instance with admin credentials","Aria Logs configured with archival destination (S3, Azure Blob, or local storage)","MCP client with tool calling support"],"input_types":["retention policy object: retention_days (integer), archive_after_days (integer), archive_destination (string), apply_to_indices (array of index names)","optional: compliance framework (GDPR, HIPAA, SOC2) for predefined policy templates"],"output_types":["policy application result: success (boolean), affected_indices (array), estimated_storage_freed_gb (number), archival_job_id (string)","current retention status: retention_days, archive_after_days, total_logs_archived, total_storage_archived_gb, next_archival_run (timestamp)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["VMware Aria Operations for Logs instance (v8.0+)","API credentials (username/password or API token) with read access to log indices","Network connectivity to Aria Logs API endpoint (typically port 9200 or 443)","MCP client supporting tool calling (Claude, Anthropic SDK, or compatible)","VMware Aria Operations for Logs instance with indexed log data","Logs must follow semi-structured format (key=value, JSON, or syslog with consistent fields)","Minimum 100 log events to produce meaningful clusters","MCP client with tool calling support","VMware vRealize Operations instance (v8.0+) with API enabled","vROps API credentials (read-only access to metrics, alerts, and topology)"],"failure_modes":["Query translation layer may not support all advanced KQL features (regex, complex boolean logic); falls back to simple field matching","Result pagination limited by MCP message size constraints — large result sets require multiple round-trips","No built-in query optimization or cost estimation for expensive searches across multi-terabyte log stores","Clustering accuracy depends on log message structure consistency — unstructured or highly variable logs may produce false negatives","Stormbreaker engine processes logs in-memory; clustering large datasets (>1M events) may timeout or consume significant memory","No machine learning — uses deterministic signature matching, so novel error patterns may not cluster until manually added to signature library","Requires pre-indexed logs in Aria; cannot cluster raw syslog or unstructured text streams","Requires separate vROps instance and API credentials; adds latency (typically 500ms-2s per correlation query)","Correlation logic is heuristic-based (time window matching, resource name matching) — may produce false correlations if naming conventions are inconsistent","vROps API rate limits may throttle correlation queries during high-volume incident scenarios","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.9,"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":"2026-05-03T15:19:13.220Z","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=apollion69-vmware-aria-logs","compare_url":"https://unfragile.ai/compare?artifact=apollion69-vmware-aria-logs"}},"signature":"L+sFJRtvp39wsQxzsWDmWNKbV9fbne5UytR7wYmmhuaDyexvhcFfLwLt+oqCnVtrEwdCr99xae3x8EySpiZ0Aw==","signedAt":"2026-06-17T06:39:34.153Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/apollion69-vmware-aria-logs","artifact":"https://unfragile.ai/apollion69-vmware-aria-logs","verify":"https://unfragile.ai/api/v1/verify?slug=apollion69-vmware-aria-logs","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"}}