{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46115018","slug":"mcp-traffic-analyze-with-npm","name":"MCP Traffic Analyze with NPM","type":"mcp","url":"https://www.npmjs.com/package/@mcp-shark/mcp-shark","page_url":"https://unfragile.ai/mcp-traffic-analyze-with-npm","categories":["mcp-servers"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46115018__cap_0","uri":"capability://tool.use.integration.mcp.server.traffic.inspection.and.analysis","name":"mcp server traffic inspection and analysis","description":"Analyzes HTTP/network traffic flowing through Model Context Protocol (MCP) server instances by instrumenting the MCP transport layer to capture, log, and expose request/response payloads, latency metrics, and error patterns. 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Implements a logging middleware that hooks into the MCP server's message processing pipeline to record each interaction without buffering, enabling real-time visibility into server activity.","intents":["Export MCP traffic logs to a file or external logging service (e.g., CloudWatch, Datadog) for audit trails","Correlate MCP requests with external events by matching timestamps and request IDs across systems","Build dashboards showing MCP server activity patterns and tool usage frequency over time","Replay MCP traffic for testing or reproducing bugs in isolated environments"],"best_for":["DevOps teams managing MCP server deployments who need audit logs and compliance records","Developers building observability dashboards for MCP-based systems","QA engineers reproducing and testing edge cases in MCP tool behavior"],"limitations":["Structured logging output can be verbose for high-frequency tool calls — requires filtering or sampling for large-scale deployments","No built-in log rotation or compression — external log management tools recommended for long-term storage","Timestamps are server-local; clock skew between clients and servers may cause correlation issues in distributed setups"],"requires":["Node.js 16+","@mcp-shark/mcp-shark package","Writable filesystem or network access to logging endpoint"],"input_types":["MCP protocol messages","logging configuration (format, verbosity level)"],"output_types":["JSON-formatted logs","plaintext logs","structured log entries with metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46115018__cap_2","uri":"capability://data.processing.analysis.mcp.performance.metrics.collection.and.reporting","name":"mcp performance metrics collection and reporting","description":"Measures and aggregates latency, throughput, and error rates for MCP server operations by instrumenting request/response timing at the protocol boundary. Collects metrics such as per-tool response times, request queue depth, and error frequency, then exposes them via a metrics endpoint or exports them to monitoring systems. Uses timing hooks in the MCP message handler to capture wall-clock latency with minimal overhead.","intents":["Identify which MCP tools are slowest and prioritize optimization efforts","Monitor MCP server health by tracking error rates and response time percentiles (p50, p95, p99)","Capacity plan for MCP server deployments by analyzing throughput trends and resource utilization","Set up alerts when MCP tool latency exceeds SLA thresholds"],"best_for":["SRE teams managing production MCP server infrastructure","Developers profiling MCP tool performance during development","Teams integrating MCP servers with monitoring platforms (Prometheus, Grafana, DataDog)"],"limitations":["Metrics collection adds ~5-10ms overhead per request due to timing instrumentation; not suitable for ultra-low-latency use cases","Aggregation is in-memory only — metrics are lost on server restart unless explicitly exported","Does not measure tool execution time inside the tool handler itself, only MCP protocol round-trip time","Cardinality explosion risk if tool names or resource URIs have high variance (e.g., unique IDs in URIs)"],"requires":["Node.js 16+","@mcp-shark/mcp-shark package","Optional: Prometheus client library or similar for metrics export"],"input_types":["MCP protocol messages","metrics configuration (sampling rate, aggregation window)"],"output_types":["metrics in Prometheus format","JSON metrics snapshots","latency histograms","error rate summaries"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46115018__cap_3","uri":"capability://data.processing.analysis.mcp.traffic.filtering.and.sampling.for.cost.performance.optimization","name":"mcp traffic filtering and sampling for cost/performance optimization","description":"Selectively captures and logs MCP traffic based on configurable rules (e.g., log only errors, sample 10% of successful requests, exclude specific tools) to reduce storage and processing overhead. Implements a rule engine that evaluates each MCP message against filter criteria before deciding whether to log or analyze it, enabling fine-grained control over observability costs.","intents":["Reduce logging costs in high-throughput MCP deployments by sampling only a percentage of requests","Focus debugging on specific tools or resource types by filtering traffic to only relevant messages","Exclude noisy or high-frequency tools from logs to improve signal-to-noise ratio","Implement sampling strategies that capture 100% of errors but only 1% of successful requests"],"best_for":["Teams running high-volume MCP servers where logging every request is cost-prohibitive","Developers debugging specific tools without being overwhelmed by traffic from other tools","Organizations with strict data retention policies who need to minimize logged data volume"],"limitations":["Sampling strategies may miss rare edge cases or intermittent bugs that occur in unsampled traffic","Filter rules require manual tuning and may need adjustment as tool usage patterns change","No adaptive sampling — sampling rates are static and do not adjust based on error rates or load","Filtered-out traffic is discarded and cannot be recovered for later analysis"],"requires":["Node.js 16+","@mcp-shark/mcp-shark package","Filter configuration (rule definitions, sampling rates)"],"input_types":["MCP protocol messages","filter rules (JSON or code-based configuration)"],"output_types":["filtered logs","sampled traffic metrics","filter statistics (% of traffic captured)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46115018__cap_4","uri":"capability://tool.use.integration.mcp.client.server.interaction.tracing.with.request.correlation","name":"mcp client-server interaction tracing with request correlation","description":"Traces individual MCP requests from client initiation through server processing to response delivery, assigning unique trace IDs and propagating them through the call chain to enable end-to-end visibility. Implements trace context injection into MCP messages and correlates logs/metrics across multiple MCP calls that are part of the same logical operation. Uses standard trace ID propagation patterns (similar to W3C Trace Context) adapted for MCP's JSON-RPC protocol.","intents":["Understand the full sequence of MCP tool calls triggered by a single client request","Debug distributed MCP setups where multiple servers collaborate on a single operation","Correlate MCP traffic with external system logs (e.g., database queries, API calls) using shared trace IDs","Measure end-to-end latency for complex MCP workflows involving multiple tool calls"],"best_for":["Teams running distributed MCP architectures with multiple servers or tool chains","Developers debugging complex multi-step MCP workflows","Organizations integrating MCP with observability platforms that support distributed tracing (Jaeger, Datadog)"],"limitations":["Trace context propagation requires coordination between client and server — custom clients must be modified to pass trace IDs","Trace ID overhead adds ~50-100 bytes per MCP message; not significant but measurable in bandwidth-constrained environments","No built-in trace storage or visualization — requires integration with external tracing backend (Jaeger, Zipkin)","Trace correlation breaks if MCP calls are made asynchronously or in parallel without explicit context passing"],"requires":["Node.js 16+","@mcp-shark/mcp-shark package","Optional: OpenTelemetry SDK or similar for trace export"],"input_types":["MCP protocol messages","trace context configuration"],"output_types":["trace spans with timing information","trace context metadata (trace ID, span ID, parent span ID)","trace export in OpenTelemetry or Jaeger format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ runtime","npm or yarn package manager","@mcp-shark/mcp-shark package installed as dependency","Active MCP server instance to instrument","Node.js 16+","@mcp-shark/mcp-shark package","Writable filesystem or network access to logging endpoint","Optional: Prometheus client library or similar for metrics export","Filter configuration (rule definitions, sampling rates)","Optional: OpenTelemetry SDK or similar for trace export"],"failure_modes":["Traffic analysis adds overhead to every MCP request — suitable for debugging but not recommended for high-throughput production without sampling","Captures only MCP protocol-level traffic; does not inspect the actual tool execution or external API calls made by tools","No built-in persistence or historical analysis — traffic data is ephemeral unless explicitly logged to external storage","Limited to Node.js MCP servers; does not support Python or other language MCP implementations","Structured logging output can be verbose for high-frequency tool calls — requires filtering or sampling for large-scale deployments","No built-in log rotation or compression — external log management tools recommended for long-term storage","Timestamps are server-local; clock skew between clients and servers may cause correlation issues in distributed setups","Metrics collection adds ~5-10ms overhead per request due to timing instrumentation; not suitable for ultra-low-latency use cases","Aggregation is in-memory only — metrics are lost on server restart unless explicitly exported","Does not measure tool execution time inside the tool handler itself, only MCP protocol round-trip time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.2,"ecosystem":0.36,"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":"2026-05-24T12:16:23.326Z","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-traffic-analyze-with-npm","compare_url":"https://unfragile.ai/compare?artifact=mcp-traffic-analyze-with-npm"}},"signature":"kdzSmAasmAXR0UGDDTa3k3jWJKx46ah0un3rXXXf375Ij0H+GBiXH1LFwKz4fT6b+89bNjZev8XLk5bJsZ2gCw==","signedAt":"2026-06-20T20:20:55.205Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-traffic-analyze-with-npm","artifact":"https://unfragile.ai/mcp-traffic-analyze-with-npm","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-traffic-analyze-with-npm","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"}}