{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-45967204","slug":"mcp-server-for-opentelemetry","name":"MCP Server for OpenTelemetry","type":"mcp","url":"https://github.com/traceloop/opentelemetry-mcp-server","page_url":"https://unfragile.ai/mcp-server-for-opentelemetry","categories":["mcp-servers","deployment-infra"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-45967204__cap_0","uri":"capability://tool.use.integration.opentelemetry.trace.collection.and.export.via.mcp.protocol","name":"opentelemetry trace collection and export via mcp protocol","description":"Exposes OpenTelemetry trace data (spans, metrics, logs) through the Model Context Protocol (MCP) interface, allowing Claude and other MCP-compatible clients to query and analyze observability data without direct instrumentation. Implements MCP resource and tool handlers that translate OpenTelemetry SDK exports into structured JSON payloads compatible with LLM consumption, bridging observability backends (Jaeger, Datadog, etc.) with AI-driven analysis workflows.","intents":["Query distributed traces from my application to understand performance bottlenecks","Analyze span data and metrics programmatically through Claude's natural language interface","Integrate observability data into AI-powered debugging and root cause analysis workflows","Export OpenTelemetry signals to Claude without building custom API layers"],"best_for":["DevOps engineers and SREs integrating observability with AI-assisted incident response","Platform teams building internal tools that combine tracing with LLM analysis","Developers debugging distributed systems using Claude as an analytical interface"],"limitations":["Requires OpenTelemetry SDK already instrumented in target application — does not auto-instrument code","Performance depends on trace volume and cardinality; high-throughput systems may need sampling configuration","MCP transport adds latency for real-time trace queries; best suited for post-incident analysis rather than live monitoring","Limited to trace data types supported by OpenTelemetry spec; custom attributes require explicit schema mapping"],"requires":["OpenTelemetry SDK 1.0+ installed in application being traced","MCP client implementation (Claude desktop, or compatible MCP host)","Python 3.8+ or Node.js 16+ (depending on MCP server runtime)","Network connectivity between MCP server and OpenTelemetry exporter endpoint"],"input_types":["OpenTelemetry trace exports (OTLP protocol)","Span context (trace ID, span ID, parent relationships)","Metric data (counters, histograms, gauges)","Log records with trace correlation"],"output_types":["JSON-serialized trace trees with span hierarchy","Structured metric snapshots","Correlated log entries with trace context","Natural language analysis from Claude based on trace patterns"],"categories":["tool-use-integration","observability","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_1","uri":"capability://memory.knowledge.trace.aware.context.injection.for.claude.conversations","name":"trace-aware context injection for claude conversations","description":"Automatically enriches Claude's conversation context with relevant trace spans and metrics based on user queries about system behavior. Implements semantic matching between natural language questions (e.g., 'why is checkout slow?') and OpenTelemetry span attributes, then injects matched trace data into the prompt context. Uses MCP's context attachment mechanism to maintain trace lineage across multi-turn conversations.","intents":["Ask Claude about performance issues and have it automatically pull relevant traces into context","Maintain trace context across a debugging conversation without manually copying span IDs","Let Claude correlate multiple traces to identify cross-service latency patterns"],"best_for":["On-call engineers using Claude for incident triage who want traces auto-loaded","Teams building AI-powered runbooks that reference live system traces"],"limitations":["Semantic matching quality depends on span naming conventions; poorly named spans may not be retrieved","Context injection adds token overhead; large trace trees may exceed Claude's context window","No built-in deduplication of repeated trace queries across conversation turns"],"requires":["OpenTelemetry instrumentation with semantic conventions for span naming","MCP client with context attachment support","Trace backend with query API (Jaeger, Tempo, or compatible)"],"input_types":["Natural language user queries","Span attribute metadata","Metric time-series data"],"output_types":["Enriched prompt context with trace data","Claude responses informed by live system state"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_10","uri":"capability://planning.reasoning.trace.based.root.cause.analysis.with.claude.reasoning","name":"trace-based root cause analysis with claude reasoning","description":"Orchestrates multi-step root cause analysis by having Claude reason over traces, metrics, and logs to identify the underlying cause of issues. Implements a reasoning loop where Claude formulates hypotheses, requests specific traces or metrics to test them, and iteratively narrows down the root cause. Uses MCP's tool invocation pattern to enable Claude to request additional data as needed during analysis, without requiring upfront context injection.","intents":["Have Claude analyze a trace and automatically request additional data to diagnose the issue","Build interactive debugging sessions where Claude asks for specific traces based on hypotheses","Get Claude to explain the root cause with supporting evidence from traces"],"best_for":["On-call engineers using Claude for incident triage","Teams building AI-powered runbooks for common issues"],"limitations":["Analysis quality depends on trace completeness; missing instrumentation limits diagnosis","Reasoning loops can be slow for complex issues; may require multiple Claude API calls","Claude may request irrelevant traces, wasting API quota and context"],"requires":["Complete trace instrumentation across services","MCP client with tool invocation support","Claude API access with sufficient quota"],"input_types":["Initial trace or error report","Claude reasoning and hypotheses","Requested trace/metric queries"],"output_types":["Root cause analysis with evidence","Recommended remediation steps","Trace data supporting the diagnosis"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_2","uri":"capability://data.processing.analysis.multi.backend.trace.aggregation.and.normalization","name":"multi-backend trace aggregation and normalization","description":"Abstracts multiple OpenTelemetry exporters and trace backends (Jaeger, Datadog, Grafana Tempo, etc.) behind a unified MCP interface, normalizing span and metric schemas across different backend formats. Implements adapter pattern with backend-specific translators that convert proprietary trace formats into canonical OpenTelemetry JSON representation, allowing Claude to query traces from heterogeneous sources without backend-specific knowledge.","intents":["Query traces from multiple observability backends through a single Claude interface","Normalize trace data from different vendors for consistent analysis","Build backend-agnostic AI debugging workflows"],"best_for":["Organizations with multi-vendor observability stacks (e.g., Datadog + self-hosted Jaeger)","Platform teams standardizing on OpenTelemetry while migrating from legacy APM tools"],"limitations":["Backend-specific features (e.g., Datadog's custom metrics) may be lost in normalization","Requires separate credentials/API keys for each backend; no unified authentication","Adapter maintenance burden grows with number of supported backends"],"requires":["API credentials for each trace backend","OpenTelemetry SDK for canonical format","Network access to all backend APIs"],"input_types":["Jaeger API responses","Datadog trace API payloads","Tempo query results","Custom OTLP exports"],"output_types":["Normalized OpenTelemetry JSON","Unified trace tree representation"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_3","uri":"capability://automation.workflow.span.filtering.and.sampling.configuration.via.mcp.tools","name":"span filtering and sampling configuration via mcp tools","description":"Exposes OpenTelemetry sampler configuration and span filtering rules as MCP tools, allowing Claude to dynamically adjust trace collection behavior based on analysis results. Implements MCP tool handlers that map to OpenTelemetry's Sampler interface, enabling Claude to request increased sampling for specific services or span attributes when investigating issues, without requiring application restarts.","intents":["Tell Claude to increase sampling for a specific service when investigating a rare bug","Dynamically filter traces by error status or latency threshold during debugging","Adjust trace collection strategy based on Claude's analysis recommendations"],"best_for":["Teams running high-volume systems where static sampling is insufficient","On-call engineers who need to increase observability for specific issues without deployment"],"limitations":["Sampling changes only affect new spans; already-collected traces cannot be retroactively resampled","Requires sampler implementation that supports dynamic reconfiguration; not all OpenTelemetry SDKs support this","Changes are temporary and reset on application restart unless persisted externally"],"requires":["OpenTelemetry SDK with dynamic sampler support","MCP server with write permissions to sampler configuration","Application architecture allowing runtime sampler updates"],"input_types":["Sampler configuration parameters","Span attribute filter expressions","Service names and span names"],"output_types":["Updated sampler configuration","Confirmation of filter application","Metrics on sampling rate changes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_4","uri":"capability://data.processing.analysis.metric.time.series.querying.and.aggregation","name":"metric time-series querying and aggregation","description":"Provides MCP tools for querying OpenTelemetry metrics (counters, histograms, gauges) with time-range and aggregation support, translating natural language metric queries from Claude into PromQL-like expressions. Implements metric backend abstraction that supports Prometheus, Grafana, and OpenTelemetry Metrics API, with built-in aggregation functions (sum, avg, percentile, rate) and time-series downsampling for efficient context injection.","intents":["Ask Claude for CPU usage trends over the last hour and have it query metrics automatically","Calculate error rates and latency percentiles across services in a single query","Correlate metric spikes with trace data to identify root causes"],"best_for":["Teams using Prometheus or Grafana for metrics who want Claude-driven analysis","SREs building AI-assisted alerting and incident response workflows"],"limitations":["Query performance depends on metrics backend; high cardinality metrics may timeout","Aggregation functions are limited to standard statistical operations; custom business logic requires external computation","Time-series downsampling may lose detail for high-resolution analysis"],"requires":["Prometheus, Grafana, or OpenTelemetry Metrics API endpoint","Metric data with consistent labeling conventions","Network access to metrics backend"],"input_types":["Metric names","Label filters","Time ranges","Aggregation functions"],"output_types":["Time-series data points","Aggregated metric values","Statistical summaries (percentiles, rates)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_5","uri":"capability://data.processing.analysis.log.correlation.with.trace.context","name":"log correlation with trace context","description":"Implements trace-to-log correlation by matching trace IDs and span IDs in log records with OpenTelemetry trace data, exposing correlated logs as MCP resources. Uses log backend APIs (ELK, Loki, Datadog) to retrieve logs with trace context, then enriches them with span metadata for unified analysis. Enables Claude to request logs for a specific trace and receive them pre-correlated without manual trace ID copying.","intents":["Get all logs for a specific trace to understand what happened during a request","Correlate error logs with slow spans to identify which code path caused the issue","Build incident timelines by combining logs, traces, and metrics"],"best_for":["Teams with structured logging and trace instrumentation","On-call engineers investigating complex multi-service incidents"],"limitations":["Requires trace IDs to be present in log records; legacy logs without trace context cannot be correlated","Log volume may exceed context limits; large traces may produce too many logs for Claude to analyze","Log backend query performance varies; correlation queries may be slow on high-volume systems"],"requires":["OpenTelemetry trace IDs injected into application logs","Log backend with trace ID search support (ELK, Loki, Datadog, etc.)","Structured logging format with trace context fields"],"input_types":["Trace IDs","Span IDs","Log query filters","Time ranges"],"output_types":["Correlated log entries with trace metadata","Unified log-trace timelines"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_6","uri":"capability://memory.knowledge.span.attribute.schema.discovery.and.validation","name":"span attribute schema discovery and validation","description":"Introspects OpenTelemetry span attributes across collected traces to build a dynamic schema of available attributes, span types, and semantic conventions. Exposes this schema as MCP resources, allowing Claude to discover what span attributes are available and validate queries against the schema before execution. Implements schema caching with periodic updates to track schema evolution as new span types are introduced.","intents":["Discover what attributes are available on database spans without reading documentation","Validate that a trace query will work before Claude attempts it","Understand the structure of spans in the system for better analysis"],"best_for":["Teams with complex, evolving instrumentation who want self-documenting trace schemas","Platform teams building internal tools that need to adapt to changing span structures"],"limitations":["Schema discovery requires sampling traces; rare span types may not be discovered","Schema updates are eventually consistent; new attributes may not appear immediately","High cardinality attributes (e.g., user IDs) may inflate schema size"],"requires":["Access to trace data with diverse span types","OpenTelemetry SDK with semantic convention compliance"],"input_types":["Collected spans","Attribute metadata"],"output_types":["JSON schema of available span attributes","Semantic convention mappings","Attribute cardinality statistics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_7","uri":"capability://planning.reasoning.trace.based.performance.regression.detection","name":"trace-based performance regression detection","description":"Analyzes historical trace data to establish baseline performance metrics (latency percentiles, error rates) and detects deviations that indicate regressions. Implements statistical comparison of recent spans against historical baselines, exposing regression alerts as MCP resources that Claude can query. Uses time-series analysis to identify which services or operations have degraded performance, enabling Claude to correlate regressions with recent changes.","intents":["Detect when a service's latency has degraded compared to historical baselines","Identify which specific operations are slower than usual","Correlate performance regressions with recent deployments or code changes"],"best_for":["Teams with continuous deployment who want automated regression detection","SREs building AI-assisted performance monitoring"],"limitations":["Baseline calculation requires sufficient historical data; new services may not have baselines","Seasonal patterns (e.g., higher traffic at certain times) may cause false positives","Requires consistent span naming and attributes; inconsistent instrumentation reduces detection accuracy"],"requires":["Historical trace data (minimum 1-2 weeks for reliable baselines)","Consistent span naming and attributes across deployments","Statistical analysis library (e.g., NumPy, SciPy)"],"input_types":["Historical span latencies","Recent span data","Baseline configuration parameters"],"output_types":["Regression alerts with severity","Affected services and operations","Deviation magnitude (e.g., '50% slower than baseline')"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_8","uri":"capability://data.processing.analysis.distributed.trace.visualization.and.dependency.mapping","name":"distributed trace visualization and dependency mapping","description":"Reconstructs service dependency graphs from trace data by analyzing span parent-child relationships and service names, exposing the dependency map as MCP resources. Generates visual representations (ASCII or JSON) of trace trees showing request flow across services, latency at each hop, and error propagation. Enables Claude to understand system architecture from traces and identify bottlenecks in request paths.","intents":["Visualize how a request flows through multiple services","Identify which service in a chain is causing latency","Understand service dependencies from actual traffic patterns"],"best_for":["Teams debugging complex microservice interactions","Architects understanding actual vs. documented service dependencies"],"limitations":["Dependency map is only as complete as trace coverage; untraced services won't appear","Visualization quality depends on span naming consistency; poorly named spans create confusing graphs","Large traces with many spans may produce unreadable visualizations"],"requires":["Distributed tracing with parent-child span relationships","Service names in span attributes","Sufficient trace coverage across services"],"input_types":["Trace data with span hierarchy","Service names","Span latencies"],"output_types":["ASCII trace trees","JSON dependency graphs","Service interaction diagrams"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-45967204__cap_9","uri":"capability://planning.reasoning.anomaly.detection.in.trace.patterns","name":"anomaly detection in trace patterns","description":"Applies statistical and machine learning techniques to identify unusual patterns in trace data, such as unexpected error rates, latency spikes, or unusual span sequences. Implements anomaly detection algorithms (isolation forest, z-score analysis) that learn normal trace behavior and flag deviations as MCP resources. Enables Claude to ask 'what's unusual about this trace?' and receive anomaly explanations without manual threshold configuration.","intents":["Automatically detect unusual patterns in traces that might indicate issues","Identify traces that are statistically different from normal behavior","Get Claude to explain what's anomalous about a specific trace"],"best_for":["Teams with high-volume systems where manual anomaly detection is infeasible","SREs building unsupervised incident detection"],"limitations":["Anomaly detection requires training data; new systems need warm-up period","False positive rate depends on algorithm tuning; may require threshold adjustment","Computationally expensive for very high-volume trace streams"],"requires":["Historical trace data for training (minimum 1-2 weeks)","Machine learning library (scikit-learn, PyOD, etc.)","Sufficient computational resources for model training"],"input_types":["Trace metrics (latency, error rate, span count)","Span attributes","Historical trace patterns"],"output_types":["Anomaly scores","Anomaly explanations","Affected traces and services"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["OpenTelemetry SDK 1.0+ installed in application being traced","MCP client implementation (Claude desktop, or compatible MCP host)","Python 3.8+ or Node.js 16+ (depending on MCP server runtime)","Network connectivity between MCP server and OpenTelemetry exporter endpoint","OpenTelemetry instrumentation with semantic conventions for span naming","MCP client with context attachment support","Trace backend with query API (Jaeger, Tempo, or compatible)","Complete trace instrumentation across services","MCP client with tool invocation support","Claude API access with sufficient quota"],"failure_modes":["Requires OpenTelemetry SDK already instrumented in target application — does not auto-instrument code","Performance depends on trace volume and cardinality; high-throughput systems may need sampling configuration","MCP transport adds latency for real-time trace queries; best suited for post-incident analysis rather than live monitoring","Limited to trace data types supported by OpenTelemetry spec; custom attributes require explicit schema mapping","Semantic matching quality depends on span naming conventions; poorly named spans may not be retrieved","Context injection adds token overhead; large trace trees may exceed Claude's context window","No built-in deduplication of repeated trace queries across conversation turns","Analysis quality depends on trace completeness; missing instrumentation limits diagnosis","Reasoning loops can be slow for complex issues; may require multiple Claude API calls","Claude may request irrelevant traces, wasting API quota and context","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.32,"ecosystem":0.56,"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":"2026-06-17T09:51:04.691Z","last_scraped_at":"2026-05-04T08:10:01.171Z","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=mcp-server-for-opentelemetry","compare_url":"https://unfragile.ai/compare?artifact=mcp-server-for-opentelemetry"}},"signature":"cgDzyEJfwjOGrcQFtmsCgWPlt/8RqKoyM9tt5POsNxC3rIfRVVmNWfMJEH1HOnsgRFlVGnL0IRWhkh9rpyulDA==","signedAt":"2026-06-20T21:21:19.815Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-server-for-opentelemetry","artifact":"https://unfragile.ai/mcp-server-for-opentelemetry","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-server-for-opentelemetry","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"}}