# GitHub Copilot Dashboard > Source: > Published: 2026-08-12 00:00:00+00:00 This dashboard provides a comprehensive view of `GitHub Copilot Chat` using the OpenTelemetry traces the Copilot Chat extension exports natively. Every panel keys off the GenAI semantic-convention attributes Copilot emits: `gen_ai.operation.name` (`invoke_agent` , `chat` , `execute_tool` , `embeddings` ), `gen_ai.response.model` , `gen_ai.usage.*` , `gen_ai.tool.name` , `gen_ai.agent.name` , and `gen_ai.conversation.id` . Use the `Service` picker at the top to scope every panel to one or more Copilot rollouts. Token panels are deliberately scoped to `gen_ai.operation.name = 'chat'` . The `invoke_agent` span carries a roll-up of its children's token counts, so summing across every span would roughly double count. Copilot also emits no `gen_ai.usage.total_tokens` , which is why the total is computed as input plus output. ## Dashboard Preview 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 the cost, performance, and reliability of GitHub Copilot Chat using OpenTelemetry trace data to help you: **Track Token Spend**: See total, cached, and reasoning tokens across model calls, and how they trend over time.** Measure Cache Savings**: Watch the prompt cache hit rate, the single biggest lever on Copilot cost, since cached input is billed at a large discount.**Compare Models**: Break down call volume and token consumption per model to spot when Copilot silently routes work to a different one.** Monitor Perceived Latency**: Track time to first chunk alongside end-to-end model call latency, so you see what developers actually experience as slowness.**Understand Tool Activity**: Identify which tools the agent reaches for, which are slowest, and how much traffic your MCP servers are earning.** See Background Work**: Surface internal agents such as title generation that spend tokens without any visible chat turn.** Catch Errors Early**: Watch failing spans over time and drill straight into the conversation turn that produced them. ## Panels Included ### Summary (Top Rows) | Panel | Type | What It Shows | |---|---|---| Total Tokens | Value | Input plus output tokens across `chat` spans, summed because Copilot emits no `total_tokens` attribute | Model Calls | Value | Count of `chat` spans, the completions requested from the model | Tool Calls | Value | Count of `execute_tool` spans, such as `read_file` , `grep_search` , or an MCP tool | Conversations | Value | Distinct chat conversations, counted on `gen_ai.conversation.id` | Prompt Cache Hit Rate | Value | Share of input tokens served from the prompt cache, as a percentage | Cached Input Tokens | Value | Input tokens read from the prompt cache rather than reprocessed | Reasoning Tokens | Value | Output tokens spent on internal reasoning, billed as output but never shown in the chat | Avg Input Tokens per Call | Value | Average prompt size per model call, which rises as the agent carries more context per turn | ### Token Usage **Token Usage Over Time**: Time-series graph of input, output, cached-input, and reasoning tokens. Cached input is a subset of input, not an additional charge.**Tokens by Model**: Pie chart of total tokens grouped by`gen_ai.response.model` , showing which model consumes the budget. Copilot routes different tasks to different models, so this rarely matches the call-count split. ### Model Activity & Latency **Model Calls Over Time**: Time-series graph of`chat` span volume grouped by`gen_ai.response.model` , useful for spotting when Copilot switches you to a different model.**Finish Reasons**: Pie chart of`gen_ai.response.finish_reasons` . A rising share of`["length"]` means answers are being truncated by the token limit, and`["tool_calls"]` means the model handed control back to the agent.**Model Call Latency**: Time-series graph of p50, p95, and p99 duration for`chat` spans.**Time to First Chunk**: Time-series graph of p50 and p95`gen_ai.response.time_to_first_chunk` , which is what a developer perceives as Copilot being slow, independent of total response length. ### Tools **Tool Calls by Name**: Bar chart of`execute_tool` spans grouped by`gen_ai.tool.name` , showing what kind of work Copilot is trusted with.**MCP vs Built-in Tools**: Pie chart splitting tool calls between MCP server tools (names prefixed`mcp_` ) and Copilot's built-in tools, so you can see whether your MCP servers are earning their place in the tool list.**Tool Latency (p95)**: Table of tools ranked by p95 execution time. Long-running terminal commands and remote MCP calls dominate here and directly stall the agent. ### Agent Activity & Errors **Activity by Operation**: Pie chart of spans across`chat` ,`execute_tool` ,`invoke_agent` , and`embeddings` .**Activity by Agent**: Bar chart of spans grouped by`gen_ai.agent.name` , which surfaces background agents such as title generation that spend real tokens without any visible chat turn.**Errors Over Time**: Time-series graph of Copilot spans with an error status, most often a tool that failed rather than the model itself.** Recent Errors**: List of individual failing spans with their status message, duration, and trace ID, so you can click through to the conversation turn that produced the failure.