{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-dash0","slug":"dash0","name":"Dash0","type":"mcp","url":"https://github.com/dash0hq/mcp-dash0","page_url":"https://unfragile.ai/dash0","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-dash0__cap_0","uri":"capability://tool.use.integration.opentelemetry.resource.navigation.and.discovery","name":"opentelemetry resource navigation and discovery","description":"Enables traversal and discovery of OpenTelemetry-instrumented resources through MCP protocol integration with Dash0's backend. Implements resource enumeration via standardized OTel semantic conventions, allowing clients to browse services, traces, metrics, and logs hierarchically without direct API calls. Uses MCP's tool-calling interface to expose Dash0's resource graph as queryable endpoints.","intents":["I need to explore all services and resources currently instrumented in my Dash0 workspace","I want to understand the dependency graph between services and their telemetry sources","I need to programmatically list available metrics, logs, and traces for a specific service"],"best_for":["DevOps engineers managing multi-service observability infrastructure","SREs investigating incident scope across distributed systems","Platform teams building internal observability dashboards"],"limitations":["Resource discovery latency depends on Dash0 backend response time; no local caching of resource graph","Limited to resources already instrumented with OpenTelemetry; non-OTel services invisible","No real-time resource change notifications — requires polling for new services"],"requires":["Dash0 account with active workspace","OpenTelemetry instrumentation deployed in target services","Valid Dash0 API credentials or authentication token","MCP client supporting tool-calling protocol"],"input_types":["service name (string)","resource type filter (enum: service, metric, trace, log)","optional time range (ISO 8601 timestamps)"],"output_types":["structured JSON resource metadata","hierarchical resource tree","resource identifiers for downstream queries"],"categories":["tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dash0__cap_1","uri":"capability://planning.reasoning.incident.investigation.and.context.aggregation","name":"incident investigation and context aggregation","description":"Aggregates metrics, logs, and traces for a specific incident or time window through coordinated MCP tool calls to Dash0 backend. Implements multi-signal correlation by querying related telemetry streams simultaneously and returning unified context, enabling rapid root-cause analysis without manual dashboard navigation. Uses Dash0's incident detection or user-specified time ranges to scope queries.","intents":["I need to pull all relevant telemetry (metrics, logs, traces) for an incident that occurred at a specific timestamp","I want to correlate error logs with corresponding trace spans and metric spikes to identify root cause","I need to export incident context for post-mortem analysis or escalation to another team"],"best_for":["On-call engineers responding to production incidents","SREs performing root-cause analysis on service degradation","Incident response teams building automated triage workflows"],"limitations":["Aggregation latency scales with query complexity; large time windows may timeout","Correlation logic is basic — no ML-based anomaly linking across signals","Limited to Dash0-indexed data; external system logs or metrics not included"],"requires":["Dash0 workspace with active metrics, logs, and trace collection","Service instrumented with OpenTelemetry for trace correlation","Incident timestamp or time range (ISO 8601 format)","MCP client with concurrent tool-calling support"],"input_types":["incident ID or timestamp (string/ISO 8601)","service name (string)","optional signal filters (array: 'metrics', 'logs', 'traces')","optional time window duration (seconds)"],"output_types":["unified incident context JSON","correlated trace spans with error logs","metric time-series data around incident window","structured error stack traces and log messages"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dash0__cap_2","uri":"capability://data.processing.analysis.metrics.querying.and.time.series.retrieval","name":"metrics querying and time-series retrieval","description":"Executes PromQL-compatible or Dash0-native metric queries against stored time-series data, returning aggregated results for specific time windows and granularities. Implements metric selection via semantic conventions (e.g., 'http.server.duration', 'system.cpu.usage') and supports common aggregations (rate, histogram percentiles, sum). Results are returned as structured time-series with timestamps and values for downstream analysis or visualization.","intents":["I need to query CPU, memory, or request latency metrics for a service over the past hour","I want to calculate the 95th percentile response time for an API endpoint","I need to retrieve raw metric data for custom analysis or export to external tools"],"best_for":["SREs building custom monitoring dashboards","Data analysts performing capacity planning analysis","Developers debugging performance regressions"],"limitations":["Query language support depends on Dash0 backend — may not support full PromQL syntax","Metric retention policies limit historical data availability (typically 30-90 days)","High-cardinality metrics (e.g., per-user request counts) may be downsampled or unavailable"],"requires":["Metrics actively collected and stored in Dash0","OpenTelemetry metrics instrumentation in target service","Valid metric names matching OTel semantic conventions","Time range specified in ISO 8601 format"],"input_types":["metric name (string, e.g., 'http.server.duration')","service/resource filter (string)","time range: start and end (ISO 8601 timestamps)","optional aggregation function (enum: rate, sum, avg, p95, p99)","optional grouping dimensions (array of label names)"],"output_types":["time-series data points (timestamp, value pairs)","aggregated scalar values","histogram buckets with counts","JSON or CSV format"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dash0__cap_3","uri":"capability://search.retrieval.logs.querying.and.filtering.with.structured.search","name":"logs querying and filtering with structured search","description":"Executes structured log queries against Dash0's log storage using field-based filtering, regex patterns, and time-range constraints. Implements log retrieval via MCP tools that support filtering by service, log level, error type, and custom attributes. Returns paginated log entries with full context (timestamps, severity, structured fields) suitable for investigation or export.","intents":["I need to find all ERROR-level logs from a specific service in the past 30 minutes","I want to search logs for a specific error message or exception type across all services","I need to export logs matching certain criteria for compliance or debugging purposes"],"best_for":["Developers debugging application errors","SREs investigating service failures","Compliance teams auditing system behavior"],"limitations":["Log query performance degrades with very large result sets (>100k logs); pagination required","Full-text search may not support complex regex patterns depending on backend indexing","Log retention policies limit historical queries (typically 7-30 days for detailed logs)"],"requires":["Logs actively collected and indexed in Dash0","OpenTelemetry or standard logging instrumentation in services","Time range specified in ISO 8601 format","MCP client supporting paginated result handling"],"input_types":["service name or resource filter (string)","log level filter (enum: DEBUG, INFO, WARN, ERROR, FATAL)","search query (string, supports regex or field:value syntax)","time range: start and end (ISO 8601 timestamps)","optional pagination parameters (limit, offset)"],"output_types":["structured log entries (JSON objects with timestamp, level, message, attributes)","paginated result sets with total count","raw log text with metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dash0__cap_4","uri":"capability://search.retrieval.distributed.trace.retrieval.and.span.correlation","name":"distributed trace retrieval and span correlation","description":"Retrieves distributed traces from Dash0's trace backend using trace IDs, span filters, or service-based queries. Implements trace reconstruction by fetching all spans belonging to a trace and correlating them by parent-child relationships, returning the full call graph with timing and error information. Supports filtering spans by service, operation name, duration, or error status.","intents":["I need to fetch a complete distributed trace for a specific request to understand the full call path","I want to find all traces where a specific service exceeded a latency threshold","I need to identify which service in a trace chain is causing the slowdown"],"best_for":["Developers debugging slow requests across microservices","SREs analyzing service dependency performance","Performance engineers identifying bottlenecks in distributed systems"],"limitations":["Trace retrieval latency depends on span count; traces with >1000 spans may be slow to reconstruct","Trace retention policies limit historical trace availability (typically 7-30 days)","Sampling may cause missing spans if not all requests are traced"],"requires":["OpenTelemetry tracing instrumentation in all services","Traces actively collected and stored in Dash0","Trace ID or service/operation filter for query","MCP client supporting nested/hierarchical result structures"],"input_types":["trace ID (string, UUID format)","optional service name filter (string)","optional operation name filter (string)","optional duration threshold (milliseconds)","optional error filter (boolean: true for error traces only)"],"output_types":["trace object with nested span hierarchy","span details: operation name, duration, status, attributes, events","parent-child span relationships","error information and stack traces"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dash0__cap_5","uri":"capability://tool.use.integration.mcp.tool.schema.registration.and.function.binding","name":"mcp tool schema registration and function binding","description":"Registers Dash0 query capabilities as standardized MCP tools with JSON Schema definitions, enabling LLM clients and MCP-compatible agents to discover and invoke observability functions. Implements tool discovery via MCP's tools/list endpoint and execution via tools/call, with automatic parameter validation against schemas. Supports both simple queries (single metric) and complex operations (multi-signal incident investigation).","intents":["I want my LLM agent to automatically discover available Dash0 query capabilities without hardcoding function names","I need to invoke Dash0 queries from an agentic workflow with automatic parameter validation","I want to expose Dash0 observability as composable tools for multi-step reasoning tasks"],"best_for":["AI engineers building LLM-powered incident response agents","Teams integrating Dash0 into broader MCP-based automation platforms","Developers creating autonomous observability workflows"],"limitations":["Tool schema complexity may limit LLM's ability to use advanced query features","No built-in tool result caching — repeated queries incur full latency","Tool execution errors may not provide sufficient context for LLM error recovery"],"requires":["MCP server implementation (Python or Node.js)","MCP client supporting tools/list and tools/call protocols","Dash0 API credentials configured in MCP server","JSON Schema validation library"],"input_types":["tool name (string)","tool parameters (JSON object matching schema)","optional context from previous tool calls"],"output_types":["tool execution result (JSON)","error messages with diagnostic information","structured tool metadata for discovery"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"moderate","permissions":["Dash0 account with active workspace","OpenTelemetry instrumentation deployed in target services","Valid Dash0 API credentials or authentication token","MCP client supporting tool-calling protocol","Dash0 workspace with active metrics, logs, and trace collection","Service instrumented with OpenTelemetry for trace correlation","Incident timestamp or time range (ISO 8601 format)","MCP client with concurrent tool-calling support","Metrics actively collected and stored in Dash0","OpenTelemetry metrics instrumentation in target service"],"failure_modes":["Resource discovery latency depends on Dash0 backend response time; no local caching of resource graph","Limited to resources already instrumented with OpenTelemetry; non-OTel services invisible","No real-time resource change notifications — requires polling for new services","Aggregation latency scales with query complexity; large time windows may timeout","Correlation logic is basic — no ML-based anomaly linking across signals","Limited to Dash0-indexed data; external system logs or metrics not included","Query language support depends on Dash0 backend — may not support full PromQL syntax","Metric retention policies limit historical data availability (typically 30-90 days)","High-cardinality metrics (e.g., per-user request counts) may be downsampled or unavailable","Log query performance degrades with very large result sets (>100k logs); pagination required","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":0.6,"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":"2026-06-17T09:51:03.037Z","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=dash0","compare_url":"https://unfragile.ai/compare?artifact=dash0"}},"signature":"npGctyj9VMJ5iT927Z895bcW3xcgMTWhS2NrD6JhCeT0JixwGmxvZo/eAXkJbuc9HT7IfTUwstDrlEq7pT3LBQ==","signedAt":"2026-06-19T13:07:05.351Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dash0","artifact":"https://unfragile.ai/dash0","verify":"https://unfragile.ai/api/v1/verify?slug=dash0","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"}}