MCP Traffic Analyze with NPM
MCP ServerFreeShow HN: MCP Traffic Analyze with NPM
Capabilities5 decomposed
mcp server traffic inspection and analysis
Medium confidenceAnalyzes 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. Works by intercepting MCP protocol messages at the server boundary before they reach tool handlers, enabling visibility into client-server communication without modifying individual tool implementations.
Provides MCP-specific traffic instrumentation as an npm package, integrating directly into the MCP server lifecycle rather than requiring external proxy tools or network-level packet capture. Uses MCP's native middleware/hook patterns to intercept protocol messages with minimal code changes.
More lightweight and MCP-native than generic HTTP debugging tools (Fiddler, Charles Proxy) because it operates at the MCP protocol abstraction level rather than raw TCP/HTTP, reducing noise and providing tool-aware context.
real-time mcp request/response logging with structured output
Medium confidenceCaptures and formats MCP protocol messages (requests, responses, errors) into structured logs with timestamps, message IDs, and context metadata. 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.
Integrates logging directly into the MCP server's message dispatch loop, capturing messages before tool execution, enabling correlation of requests with their outcomes. Provides structured output with MCP-specific metadata (message IDs, tool names, resource URIs) rather than generic HTTP logs.
More detailed than generic Node.js logging (Winston, Pino) because it understands MCP semantics and automatically extracts tool names, resource identifiers, and protocol-level context without custom parsing.
mcp performance metrics collection and reporting
Medium confidenceMeasures 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.
Provides MCP-aware metrics collection that understands tool semantics and resource types, allowing per-tool latency breakdowns and error categorization by tool rather than generic HTTP status codes. Integrates with the MCP server's native message dispatch to avoid external proxy overhead.
More granular than generic Node.js APM tools (New Relic, Datadog APM) because it exposes MCP-specific dimensions (tool name, resource type, method) without requiring custom instrumentation code in each tool handler.
mcp traffic filtering and sampling for cost/performance optimization
Medium confidenceSelectively 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.
Provides MCP-aware filtering that understands tool names, resource types, and error categories, allowing rules like 'log all errors from tool X but only 5% of successful calls to tool Y'. Operates at the MCP protocol level before messages are serialized, reducing memory overhead.
More efficient than post-hoc log filtering because it discards unwanted messages before they are serialized and stored, whereas generic log aggregation tools (ELK, Splunk) filter after data is already persisted.
mcp client-server interaction tracing with request correlation
Medium confidenceTraces 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.
Implements MCP-native distributed tracing that understands the protocol's JSON-RPC structure and tool semantics, automatically extracting tool names and resource URIs as span attributes. Propagates trace context through MCP's message envelope without requiring changes to tool implementations.
More integrated than generic distributed tracing (OpenTelemetry instrumentation) because it automatically instruments MCP's message dispatch without requiring manual span creation code in each tool or client.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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MCP Router
** – Free Windows and macOS app that simplifies MCP management while providing seamless app authentication and powerful log visualization by **[MCP Router](https://github.com/mcp-router/mcp-router)**
Best For
- ✓MCP server developers building and debugging custom tool implementations
- ✓Teams deploying MCP servers in production who need observability into client interactions
- ✓Developers integrating multiple MCP clients with a shared server instance
- ✓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
- ✓SRE teams managing production MCP server infrastructure
- ✓Developers profiling MCP tool performance during development
Known Limitations
- ⚠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
Requirements
Input / Output
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