SigNoz MCP Server Dashboard

SigNoz released a new dashboard for its MCP server, providing operational visibility into tool-call volume, error rates, JSON-RPC method usage, and execute_tool span latency. The dashboard, which requires SigNoz v0.135.0 or newer and uses the V2 dashboard schema, helps teams track usage, ensure reliability, pinpoint failures, and optimize performance. It includes metrics such as total tool calls, MCP span error rate, per-tool call rates, and latency percentiles (p50/p95/p99).

This dashboard provides operational visibility into the SigNoz MCP server. It tracks tool-call volume and error rates, MCP JSON-RPC method usage, and execute tool span latency broken down per tool so teams can quickly see how the server is being used, which tools are slow or failing, and whether the overall service is healthy. Dashboard Preview Dashboard JSON Recommended. Uses the V2 dashboard schema https://signoz.io/docs/dashboards/dashboards-v2-api/ and needs SigNoz v0.135.0 or newer. Import it in SigNoz with Dashboards → + New dashboard → Import JSON . Import guide https://signoz.io/docs/dashboards/import-dashboard/ What This Dashboard Monitors This dashboard tracks critical operational metrics for your SigNoz MCP server to help you: Track Usage : Monitor total tool-call volume to get a quick pulse on overall traffic and adoption of the MCP server. Ensure Reliability : Watch the MCP span error rate to get an at-a-glance signal of service health and catch sudden spikes early. Understand the Traffic Mix : See per-tool call rates to identify which tools drive the most load and how usage is distributed. Pinpoint Failures : Break down tool calls and error rates by tool to find exactly which tool is failing rather than just the aggregate. Optimize Performance : Track execute tool span latency percentiles p50/p95/p99 , including per-tool p95, to surface both typical and tail latency. Analyze Protocol Usage : Observe per-method JSON-RPC call rates to understand how clients interact with the MCP protocol. Metrics Included Overview Total Tool Calls : A single-value view of the total number of tool calls handled by the MCP server over the selected time range. It's a quick pulse on overall traffic and adoption. MCP Spans Error Rate : A single-value summary of the percentage of MCP spans that resulted in an error. It gives an at-a-glance signal of service health. Low and stable is good, sudden spikes warrant investigation. Total Tool Calls by tool : A pie chart breakdown of total tool calls by tool. It complements the rate graphs with a clear share-of-usage view across the full time range. Tool Usage Tool calls / sec by tool : Per-tool call rate, stacked to show the traffic mix across all tools. This reveals which tools are driving load and how usage is distributed. Tool calls by error flag : Tool calls split by mcp.tool.is error true = errored . This separates healthy calls from failures, making it easy to spot when errors start climbing relative to total volume. MCP method calls / sec : Per-method JSON-RPC call rate initialize , ping , tools/list , resources/list , prompts/list , and more . This shows how clients are interacting with the protocol and which methods dominate traffic. Performance & Reliability execute tool span latency : p50 / p95 / p99 of execute tool span duration. Excludes the SSE long-poll GET /mcp . Tracking these percentiles surfaces both typical performance and tail latency that affects the worst-case user experience. execute tool p95 latency by tool : p95 of execute tool span duration broken down by tool gen ai.tool.name . This identifies exactly which tool is slow, so optimization effort can be targeted. execute tool error rate % by tool : Per-tool error rate %, computed as execute tool spans with has error=true over total, grouped by tool gen ai.tool.name . This pinpoints which specific tools are failing rather than just the aggregate rate.