> ## Documentation Index
> Fetch the complete documentation index at: https://docs.latitude.so/llms.txt
> Use this file to discover all available pages before exploring further.

# Build a dashboard from Latitude data

> Give an agent arbitrarily sliceable analytics and let it render a self-contained HTML dashboard.

Latitude doesn't ship a dashboard builder. Instead it exposes one composable analytics tool,
`queryAnalytics`, over the [MCP connection](/getting-started/mcp). You ask your agent for a dashboard;
the agent makes a handful of `queryAnalytics` calls and renders a **self-contained HTML page** from the
results. Latitude provides the data; the agent renders it.

## The shape of every widget

Every chart is the same three-part query over a filtered stream:

* **Metric** — the number each row reduces to (`count`, `errorRate`, `percentile(duration)`, `avg(cost)`, …).
* **Breakdown** — the dimension to split across, one row per value (by `model`, `tool`, `signalId`, …).
* **Time bucket** — optional granularity (`hour` / `day` / `week`) that turns a number into a trend.

So "error rate by model, weekly" = metric `errorRate` × breakdown `model` × bucket `week`. Swap any
axis for the next chart — no new endpoint.

## Streams and metrics

| Stream                          | What it measures                    | Metrics                                                                                                                                                                  | Break down by                                                                                               |
| ------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- |
| `traces` / `sessions` / `spans` | requests, conversations, operations | `count`, `errorRate`, `cacheHitRate`, `sum`/`min`/`max`/`avg`/`median` of `duration`\|`cost`\|`tokens`, plus `percentile` (`{ kind: "percentile", field, p }`, `p` 1–99) | `model`, `provider`, `service`, `tool`, `tag`, `status` (+ `name`/`userId` on traces, `operation` on spans) |
| `scores` (signals)              | scored occurrences                  | `count`, `passRate`, `errorRate`, `avg`/`min`/`max`/`median` of `value`                                                                                                  | `signalId`, `source`, `model`, `provider`, `service`, `tool`, `tag`                                         |
| `behaviors`                     | clustered behaviors                 | `count`, `avg`/`min`/`max`/`median` of `confidence`                                                                                                                      | `cluster`, `session`, `method`                                                                              |
| `moments`                       | labeled conversation moments        | `count`, `avg`/`min`/`max`/`median` of `confidence`\|`coherence`                                                                                                         | `kind`, `actor`, `session`                                                                                  |

Values are returned in human units: `duration` in **seconds**, `cost` in **dollars**, and rates
(`errorRate`, `cacheHitRate`, `passRate`) as a **0–1 fraction**.

## Example calls

```jsonc theme={"theme":{"light":"github-light","dark":"github-dark"}}
// Error rate by model, weekly, last 30 days
queryAnalytics({
  stream: "traces", metric: { kind: "errorRate" }, breakdown: "model",
  timeBucket: { unit: "week" },
  range: { fromIso: "2026-05-31T00:00:00Z", toIso: "2026-06-30T00:00:00Z" }
})

// 95th-percentile latency by tool
queryAnalytics({
  stream: "traces", metric: { kind: "percentile", field: "duration", p: 95 }, breakdown: "tool",
  range: { fromIso: "2026-06-23T00:00:00Z", toIso: "2026-06-30T00:00:00Z" }
})

// Cost by provider
queryAnalytics({
  stream: "traces", metric: { kind: "sum", field: "cost" }, breakdown: "provider",
  range: { fromIso: "2026-06-23T00:00:00Z", toIso: "2026-06-30T00:00:00Z" }
})

// Conversation fallout: moment count by kind
queryAnalytics({
  stream: "moments", metric: { kind: "count" }, breakdown: "kind",
  range: { fromIso: "2026-06-23T00:00:00Z", toIso: "2026-06-30T00:00:00Z" }
})
```

Each returns a tidy series — `[{ key?, bucketStart?, value }]` — small enough to embed inline no matter
how many traces it spans.

## Row-level widgets

For a "top 10 slowest tool calls" style table (rows, not an aggregate), use `querySpans` to list the
individual spans across traces:

```jsonc theme={"theme":{"light":"github-light","dark":"github-dark"}}
querySpans({
  filters: { operation: [{ op: "eq", value: "execute_tool" }] },
  range: { fromIso: "2026-06-23T00:00:00Z", toIso: "2026-06-30T00:00:00Z" },
  limit: 10
})
```

## Rendering

The agent embeds the returned series directly into a **self-contained HTML file** (an inline chart
library, data baked in) and commits it to your repo or hosts it wherever you like. Nothing is rendered
by Latitude — which keeps the data portable and the setup dependency-free.

A good prompt is simply: *"Build a self-contained HTML dashboard of this project's LLM reliability and
cost over the last 30 days."* The agent discovers the right `queryAnalytics` calls on its own.

See [Agent dispatch](/agent-dispatch/overview#investigate-a-signal) for the investigation counterpart —
how an agent uses these same tools to root-cause a signal.