{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-comet-opik","slug":"comet-opik","name":"Comet Opik","type":"mcp","url":"https://github.com/comet-ml/opik-mcp","page_url":"https://unfragile.ai/comet-opik","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-comet-opik__cap_0","uri":"capability://search.retrieval.natural.language.llm.trace.querying","name":"natural language llm trace querying","description":"Converts natural language questions into structured queries against Opik trace databases, enabling non-SQL users to ask questions like 'show me all traces where latency exceeded 2 seconds' or 'find traces with low quality scores'. Implements an LLM-to-query translation layer that parses user intent and maps it to Opik's trace schema (spans, attributes, metrics, metadata) before executing against the backend telemetry store.","intents":["I want to find traces matching specific performance criteria without writing SQL","I need to quickly identify problematic LLM calls based on natural language descriptions","I want to explore my trace data interactively without learning Opik's query API"],"best_for":["ML engineers and product managers analyzing LLM behavior","Teams without SQL expertise who need ad-hoc trace analysis","Developers building observability dashboards on top of Opik"],"limitations":["Query translation accuracy depends on LLM understanding of Opik schema — complex nested queries may require refinement","No support for real-time streaming queries — operates on historical trace data only","Context window limits may prevent querying across very large trace datasets in single request"],"requires":["Opik instance deployed and accessible","MCP server running with network access to Opik backend","LLM client with function-calling capability (Claude, GPT-4, etc.)"],"input_types":["natural language text","conversational queries"],"output_types":["structured trace data (JSON)","aggregated metrics","filtered trace lists"],"categories":["search-retrieval","natural-language-interfaces"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_1","uri":"capability://memory.knowledge.prompt.version.and.variant.analysis","name":"prompt version and variant analysis","description":"Retrieves and compares different versions and variants of prompts stored in Opik, enabling side-by-side analysis of prompt changes and their impact on LLM outputs. Queries Opik's prompt registry to fetch version history, metadata, and associated trace performance metrics, allowing users to understand which prompt versions produced better results.","intents":["I want to see how my prompt has evolved over time and which version performed best","I need to compare two prompt variants and their corresponding trace metrics","I want to identify the prompt version associated with a specific trace"],"best_for":["Prompt engineers iterating on LLM instructions","Teams A/B testing different prompt strategies","Researchers analyzing prompt-to-output relationships"],"limitations":["Requires prompts to be explicitly registered in Opik — ad-hoc prompts not stored in registry are not queryable","No built-in diff visualization — returns raw prompt text requiring external tools for visual comparison","Performance correlation analysis is manual — no automated statistical significance testing"],"requires":["Opik instance with prompt registry enabled","Prompts previously logged/registered to Opik","MCP client connected to Opik backend"],"input_types":["natural language queries about prompts","prompt names or IDs"],"output_types":["prompt text (string)","version metadata (timestamps, authors)","associated trace metrics (JSON)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_2","uri":"capability://data.processing.analysis.trace.filtering.and.aggregation.by.custom.attributes","name":"trace filtering and aggregation by custom attributes","description":"Enables filtering traces by arbitrary custom attributes (user-defined metadata, tags, dimensions) and aggregating results across multiple dimensions (e.g., by model, by user, by cost). Implements attribute-based indexing in Opik that supports multi-dimensional grouping and statistical aggregation (sum, mean, percentile) on trace metrics.","intents":["I want to see average latency grouped by LLM model and user segment","I need to filter traces to only those tagged with 'production' and 'high-priority'","I want to calculate cost per user or cost per feature across all traces"],"best_for":["DevOps engineers monitoring LLM service health by dimension","Product teams analyzing per-user or per-feature LLM costs","Data analysts building custom observability reports"],"limitations":["Aggregation performance degrades with high cardinality attributes — querying across millions of unique user IDs may timeout","Custom attributes must be defined at trace ingestion time — retroactive attribute addition not supported","No time-series aggregation — results are point-in-time snapshots, not time-bucketed trends"],"requires":["Opik traces with custom attributes already logged","MCP server with access to Opik's attribute index","LLM client capable of parsing aggregation queries"],"input_types":["natural language filter and grouping requests","attribute names and values"],"output_types":["filtered trace lists (JSON array)","aggregated statistics (JSON with sum/mean/percentile)","grouped results (nested JSON by dimension)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_3","uri":"capability://data.processing.analysis.span.level.performance.drill.down","name":"span-level performance drill-down","description":"Allows users to navigate from high-level trace summaries down to individual spans (function calls, LLM invocations, tool calls) and analyze their performance characteristics. Queries Opik's span hierarchy to retrieve parent-child relationships, timing data, token counts, and error information for each span in a trace.","intents":["I want to see which span in my trace consumed the most tokens or took the longest","I need to identify which tool call failed and why in a multi-step trace","I want to understand the call stack and timing breakdown for a slow trace"],"best_for":["LLM application developers debugging performance bottlenecks","Teams optimizing token usage and cost","Engineers troubleshooting errors in multi-step LLM workflows"],"limitations":["Span hierarchy visualization is text-based through MCP — no interactive flame graphs or timeline views","Requires spans to be properly instrumented at application level — missing span data cannot be retroactively added","Token count accuracy depends on LLM provider's reporting — some providers may not expose granular token data per span"],"requires":["Opik instrumentation in application code to capture spans","Traces with nested span structure already logged","MCP client with ability to render hierarchical data"],"input_types":["trace IDs","natural language requests for span analysis"],"output_types":["span hierarchy (nested JSON)","timing data (milliseconds, percentiles)","token counts and cost estimates","error messages and stack traces"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_4","uri":"capability://data.processing.analysis.llm.quality.metric.querying.and.comparison","name":"llm quality metric querying and comparison","description":"Retrieves and analyzes quality metrics (accuracy, relevance, hallucination scores, user ratings) associated with traces, enabling comparison across different models, prompts, or time periods. Queries Opik's metric storage to fetch computed or user-provided quality scores and correlate them with trace characteristics.","intents":["I want to see which model version has the highest accuracy score across all traces","I need to compare quality metrics between two prompt variants","I want to identify traces with low quality scores and understand why"],"best_for":["ML teams evaluating LLM model quality","Prompt engineers measuring prompt effectiveness","Product managers tracking LLM application quality over time"],"limitations":["Quality metrics must be explicitly computed or logged — no automatic quality scoring","Metric definitions vary by use case — no standardized quality metric schema across organizations","Correlation analysis is limited to simple grouping — no statistical significance testing or causal inference"],"requires":["Opik traces with quality metrics already computed and logged","Metric definitions documented or standardized in Opik","MCP client with numeric aggregation capability"],"input_types":["natural language queries about quality","metric names and thresholds"],"output_types":["quality metric values (numeric)","aggregated statistics (mean, median, percentile)","ranked lists of traces or models by quality","correlation data (JSON)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_5","uri":"capability://data.processing.analysis.cost.analysis.and.optimization.recommendations","name":"cost analysis and optimization recommendations","description":"Analyzes token usage and API costs across traces, providing breakdowns by model, user, feature, or time period, and suggesting optimization opportunities. Queries Opik's token and cost data to compute per-trace costs, identify expensive operations, and recommend prompt or model changes.","intents":["I want to see total LLM costs broken down by model and user","I need to identify which features or users are driving the highest costs","I want to understand the cost impact of switching to a cheaper model"],"best_for":["Finance and operations teams managing LLM infrastructure costs","Product managers optimizing cost-per-feature","Engineers reducing token usage in production systems"],"limitations":["Cost calculations depend on accurate token counts from LLM providers — some providers may not expose detailed token breakdowns","Optimization recommendations are heuristic-based — no machine learning-based cost prediction","Historical cost data is only as complete as Opik's trace history — gaps in tracing lead to incomplete cost analysis"],"requires":["Opik traces with token counts and cost data logged","LLM provider pricing information configured in Opik","MCP client with numeric computation capability"],"input_types":["natural language cost analysis requests","time ranges and dimension filters"],"output_types":["cost breakdowns (JSON with currency)","cost per unit (per user, per feature, per model)","optimization recommendations (text)","trend data (cost over time)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_6","uri":"capability://data.processing.analysis.error.and.exception.analysis.across.traces","name":"error and exception analysis across traces","description":"Identifies and analyzes errors, exceptions, and failures in traces, providing aggregated error statistics, root cause analysis, and correlation with trace characteristics. Queries Opik's error data to extract exception types, stack traces, and error context, then groups and analyzes them by model, prompt, or user.","intents":["I want to see all traces that failed and understand why","I need to identify the most common error types in my LLM application","I want to know if errors are correlated with specific models or prompts"],"best_for":["DevOps and SRE teams monitoring LLM application reliability","Developers debugging production issues","Teams tracking error trends over time"],"limitations":["Error detection depends on proper exception logging in application — unhandled errors may not appear in traces","Root cause analysis is limited to error message and stack trace — no automatic correlation with external systems","Error categorization is manual — no built-in error taxonomy or classification"],"requires":["Opik traces with error/exception data logged","Application instrumentation to capture exceptions","MCP client with text analysis capability"],"input_types":["natural language error analysis requests","error type filters"],"output_types":["error lists with stack traces (JSON)","error frequency and distribution (aggregated statistics)","error correlation data (grouped by dimension)","affected traces (filtered list)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-comet-opik__cap_7","uri":"capability://data.processing.analysis.temporal.trend.analysis.and.anomaly.detection","name":"temporal trend analysis and anomaly detection","description":"Analyzes how trace metrics (latency, cost, quality) change over time and identifies anomalies or unusual patterns. Implements time-series aggregation in Opik to bucket traces by time period and compute trends, then uses statistical methods to flag deviations from baseline behavior.","intents":["I want to see how latency has trended over the past week","I need to detect if there was a sudden spike in errors or costs","I want to understand if a model change improved performance"],"best_for":["Operations teams monitoring LLM service health","Product teams tracking performance improvements","Researchers analyzing LLM behavior changes over time"],"limitations":["Anomaly detection uses simple statistical methods — no machine learning-based anomaly detection","Requires sufficient historical data to establish baseline — new services may not have enough data for meaningful trend analysis","Time-series aggregation granularity is fixed — no adaptive bucketing based on data density"],"requires":["Opik traces with timestamps spanning multiple time periods","Sufficient trace volume to compute meaningful aggregations","MCP client with numeric time-series analysis"],"input_types":["natural language trend analysis requests","time ranges and metric names"],"output_types":["time-series data (JSON with timestamps and values)","trend summaries (increasing/decreasing/stable)","anomaly flags (JSON with timestamp and severity)","baseline and current statistics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Opik instance deployed and accessible","MCP server running with network access to Opik backend","LLM client with function-calling capability (Claude, GPT-4, etc.)","Opik instance with prompt registry enabled","Prompts previously logged/registered to Opik","MCP client connected to Opik backend","Opik traces with custom attributes already logged","MCP server with access to Opik's attribute index","LLM client capable of parsing aggregation queries","Opik instrumentation in application code to capture spans"],"failure_modes":["Query translation accuracy depends on LLM understanding of Opik schema — complex nested queries may require refinement","No support for real-time streaming queries — operates on historical trace data only","Context window limits may prevent querying across very large trace datasets in single request","Requires prompts to be explicitly registered in Opik — ad-hoc prompts not stored in registry are not queryable","No built-in diff visualization — returns raw prompt text requiring external tools for visual comparison","Performance correlation analysis is manual — no automated statistical significance testing","Aggregation performance degrades with high cardinality attributes — querying across millions of unique user IDs may timeout","Custom attributes must be defined at trace ingestion time — retroactive attribute addition not supported","No time-series aggregation — results are point-in-time snapshots, not time-bucketed trends","Span hierarchy visualization is text-based through MCP — no interactive flame graphs or timeline views","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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":"2026-06-17T09:51:02.371Z","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=comet-opik","compare_url":"https://unfragile.ai/compare?artifact=comet-opik"}},"signature":"cOM5yJuC/hJS9KQMfRN4u3esv5TYn6syukt62w6Vdv/MBnmsY+eM+202Si7oZHZ8Bz1e8QmkTEMm/nGanN1zDQ==","signedAt":"2026-06-19T20:47:30.558Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/comet-opik","artifact":"https://unfragile.ai/comet-opik","verify":"https://unfragile.ai/api/v1/verify?slug=comet-opik","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"}}