> ## Documentation Index > Fetch the complete documentation index at: https://docs.coval.ai/llms.txt > Use this file to discover all available pages before exploring further. # Configure metrics > Template variables, transcript scope, and trace context for custom metrics. Many metric types are configurable: you supply a prompt, a pattern, or a threshold and Coval runs it like any built-in metric. This page covers the configuration options shared across metrics — how to inject dynamic values, focus a metric on part of a conversation, and give it visibility into your agent's traces. For prompt-writing technique itself, see [Write judge prompts](/concepts/metrics/writing-judge-prompts). ## Template variables You can embed dynamic values from agents, test cases, and uploaded conversations into your metric prompts using template variables. The system replaces each placeholder with the actual value for the simulation or conversation being evaluated, so one metric adapts to many configurations. ### `{{agent.*}}` — Agent attributes Reference custom properties defined on the [agent configuration](/concepts/agents/overview#attributes) with `{{agent.attribute_name}}`. The placeholder is replaced with the attribute value from the agent under evaluation. ``` Given the transcript, did the assistant provide the correct opening hours? The correct opening hours are: {{agent.opening_hours}} Return YES if: • The assistant stated the opening hours as {{agent.opening_hours}} • The assistant provided hours that match exactly (e.g., "9 AM to 5 PM" matches "9:00am-5:00pm") Return NO if: • The assistant provided different opening hours than {{agent.opening_hours}} • The assistant claimed not to know the opening hours ``` ### `{{expected_output.*}}` — Test case expected outputs Reference values from a test case's expected output with `{{expected_output.}}`. For a test case whose expected output carries `source`, `destination`, and `ticket_class`: ``` Given the transcript, did the assistant correctly process the flight booking request? The booking details are: - Source: {{expected_output.source}} - Destination: {{expected_output.destination}} - Ticket Class: {{expected_output.ticket_class}} Return YES if: • The assistant confirmed all three details correctly using the exact values: {{expected_output.source}}, {{expected_output.destination}}, and {{expected_output.ticket_class}} ``` You can combine agent and expected-output attributes in the same prompt for context-aware evaluations. ### `{{customer_metadata.}}` — Upload-time values Reference per-conversation values you provide at upload time with `{{customer_metadata.}}`. This is useful for evaluating monitored production calls against ground truth known only at upload time — prices, order totals, confirmation numbers, expected outcomes. Each conversation is judged against its own metadata value. See [Customer Metadata (Upload-Time Values)](/concepts/attributes/overview#customer-metadata-upload-time-values). For full documentation — nested paths, array indexing, dynamic keys, and examples — see [Attributes](/concepts/attributes/overview). *** ## Transcript scope Transcript Scope focuses metric evaluation on specific portions of a conversation rather than the entire transcript, reducing noise and improving accuracy for targeted assessments. It is available for all LLM Judge metrics (Binary, Numerical, Categorical) and Multimodal/Audio LLM Judge metrics. ### Configuration Set the **Transcript Scope** toggle to: * **Full** (default) — Evaluate the entire transcript. * **Custom** — Apply filters to focus on specific messages. Transcript Scope UI Transcript Scope UI Limit evaluation to messages from specific speakers: * **Agent** — Only assistant/agent messages * **User** — Only user/customer messages * **Both** — Messages from the selected roles Useful when you want to assess agent behavior without user input affecting the evaluation, or vice versa. Limit evaluation to a portion of the conversation: * **Last N turns** — Only the final N message exchanges * **First N turns** — Only the opening N message exchanges Useful for evaluating specific phases such as greetings, closings, or resolution attempts. | Use case | Filter configuration | | ----------------------------------- | ----------------------------- | | Evaluate only agent responses | Role filter: `agent` | | Check the closing of a conversation | Range filter: Last 3 turns | | Assess user sentiment only | Role filter: `user` | | Focus on recent context | Range filter: Last N messages | Combine filters for precise control. A Role filter (`agent`) plus a Range filter (last 5 turns) evaluates just the agent's closing performance. ### Scope for audio metrics When using Transcript Scope with [Multimodal/Audio LLM Judge](/concepts/metrics/types/llm-judge) metrics, Coval automatically: 1. Filters the transcript to the selected messages. 2. Uses message timestamps to extract the corresponding audio segments. 3. Merges adjacent segments (within 0.5 seconds) to avoid artifacts. 4. Sends only the filtered audio to the LLM. This enables focused audio evaluations — e.g., the agent's speech quality in the last 3 turns — while reducing processing time and token cost. ### Benefits * **More accurate** — Removes noise from irrelevant messages. * **Lower cost** — Processes less content per evaluation. * **Faster** — Smaller context means quicker LLM responses. *** ## Trace context Trace Context gives an LLM Judge or [Composite Evaluation](/concepts/metrics/types/llm-judge#composite-evaluation) metric visibility into what your agent actually *did* — not just what it said — by including OpenTelemetry span data alongside the transcript. When **Include Traces** is enabled on a custom transcript scope, the judge receives a `TRACE CONTEXT:` block appended to its prompt. This block summarizes the conversation's OTel spans: span names, timing windows, and key attributes like tool call names and function arguments. For Composite Evaluation metrics the same block is appended to every per-criterion prompt. ### When to enable Trace context is most useful when the behavior you want to evaluate isn't visible in the transcript alone: | Use case | Why traces help | | -------------------------------------------------------------- | ------------------------------------------------------------------------ | | Verify the agent used the right tools in the right order | Tool call spans show what functions were invoked and with what arguments | | Catch hallucinations — agent claimed to do something it didn't | Trace spans show whether the action actually occurred | | Evaluate retrieval quality | Retrieval spans show what data was fetched before the agent responded | | Assess error handling | Error spans reveal failures the agent may have silently recovered from | ### How to enable 1. Open or create a supported metric — LLM Judge (Binary, Numerical, Categorical, or Audio) or Composite Evaluation. 2. Set **Transcript Scope** to **Custom**. 3. In the custom scope panel, toggle **Include Traces** on. The trace context is appended automatically — no prompt changes are required, though referencing it explicitly improves results. ### Requirements * Your agent must emit OpenTelemetry traces to Coval. See the [OpenTelemetry Traces guide](/concepts/simulations/traces/opentelemetry) for setup. * The simulation must have produced trace data. If none is available, the toggle has no effect and the prompt is sent without a trace block. ### Writing prompts that use trace context Reference the trace data explicitly and instruct the judge to reason about both sources. For example, to verify tool usage: ``` Given the transcript and trace context, did the assistant call the `lookup_account` function before providing account balance information? Return YES if: • The TRACE CONTEXT shows a tool call to `lookup_account` (or equivalent) occurring before the agent stated the balance • The transcript confirms the agent provided balance details Return NO if: • The agent mentioned account balance but no `lookup_account` tool call appears in the TRACE CONTEXT • The tool call appears AFTER the agent stated the balance (out of order) • The TRACE CONTEXT shows a failed or missing tool call for this operation Note: If no TRACE CONTEXT is provided, evaluate based on transcript alone. ``` Add "Note: If no TRACE CONTEXT is provided, evaluate based on transcript alone" to your prompt. This makes the metric degrade gracefully on simulations where traces weren't captured.