Maple Dashboard Widgets

Maple dashboard widgets via MCP

Everything below is generated from the live widget schema by bun run --cwd apps/ai mcp:docs. Do not edit this file by hand — edit apps/api/src/mcp/lib/dashboard-schema-doc.ts and regenerate. The same module backs the describe_dashboard_schema MCP tool, so an agent at runtime and a reader here see one truth.

When to use this skill

When constructing widget JSON for mcp__maple__create_dashboard, mcp__maple__add_dashboard_widget, mcp__maple__update_dashboard_widget or mcp__maple__replace_dashboard_widgets.

For a brand-new dashboard, prefer the simplified widgets array on create_dashboard ({ title, source, metric, group_by?, service_name?, unit? }) — it fills in the traps below. Reach for raw JSON when the simplified spec can't express what you need: multi-query charts, formulas, hidden series, non-default transforms.

The three silent failures

1. A data source is a kind-discriminated union. { "endpoint": …, "params": … } is the retired v2 shape and will not decode.
2. percent means a 0–1 fraction; percent_100 means 0–100. Inverted from Grafana.
3. groupBy is ignored unless addOns.groupBy is true. No error, just an ungrouped total.

Verification

MCP success is not chart correctness. The mutation tools reject queries the engine can't honor and widgets that cannot render, and return an automatic inspect_chart_data summary for everything else. Read the verdict: suspicious or broken means fix and resubmit.

Panel types

panel_type is the whole answer to “what kind of widget is this”. Pass it and the persisted visualization, the display.chartId and the raw-SQL display type are all derived for you. The legacy visualization parameter is still accepted, but it collapses line/bar/area into chart and then needs a display.chartId to tell them apart. The two columns below are what panel_type resolves to, and are what you write directly when authoring an assembled widget rather than calling add_dashboard_widget. A panel whose chartId is — takes none: the visualization alone identifies it.

Choosing one

• line / area / bar — a value over time. area and bar accept display.stacked; line does not.
• hbar — a ranked “top N by volume”. Each row is labelled with its share of the total.
• funnel — sequential stages with a drop-off. Labels each bar as a share of the largest, so an unranked breakdown of four equal things reads “100%” four times. Use hbar for that.
• pie — composition, few slices. Collapses a long tail into “Other”.
• stat / gauge — one number. A gauge adds an arc; set display.gauge.min/max to match the unit.
• table — rows and columns; set display.columns for headers and per-column units.
• list — recent traces/logs. Configured by display.listDataSource, never by SQL.
• heatmap / histogram — a distribution. A histogram over traces can bucket raw values client-side.
• markdown — a static note. Takes no query at all.

Data sources

A widget's dataSource is a discriminated union over kind: query, raw_sql, route, static. Every arm requires its kind.

If you have seen { "endpoint": …, "params": … } anywhere — that is the retired v2 shape and it will not decode. A query source spreads queries/formulas at the TOP LEVEL, not under params, and requires resultShape.

kind: "query" — the query builder

resultShape is required and is one of timeseries (a value over time), breakdown (one row per group) or list (raw rows). Optional: formulas, comparison, limit, defaultLimit, columns, transform.

{
  "kind": "query",
  "resultShape": "timeseries",
  "queries": [
    {
      "id": "q1",
      "name": "A",
      "enabled": true,
      "whereClause": "service.name = \"api\"",
      "aggregation": "error_rate",
      "stepInterval": "",
      "orderByDirection": "desc",
      "addOns": {
        "groupBy": true,
        "having": false,
        "orderBy": false,
        "limit": false,
        "legend": false
      },
      "groupBy": [
        "service.name"
      ],
      "having": "",
      "orderBy": "",
      "limit": "",
      "legend": "",
      "dataSource": "traces"
    }
  ]
}

kind: "raw_sql" — your own ClickHouse SQL

{
  "kind": "raw_sql",
  "sql": "SELECT count() AS value FROM logs WHERE $__orgFilter",
  "displayType": "stat"
}

kind: "static" — a markdown note, no request

{
  "kind": "static"
}

kind: "route" — a curated built-in panel

{ "kind": "route", "endpoint": "service_overview", "params": { … } }. These back the prebuilt panels; you rarely author one by hand.

Scalar panels need a reduction

A stat or gauge reads data[0].value. Without transform.reduceToValue it renders [object Object]. add_dashboard_widget injects { field: "value", aggregate: "first" } when you omit it; set it explicitly to choose a different reducer. Valid aggregates: sum, first, count, avg, max, min — there is no last.

Which one depends on what the query returns, because the query is bucketed over time and the reducer collapses those buckets into one number:

• A rate or a count (count, a metrics rate) → sum, for a window total.
• A latency percentile or an average (p95_duration, avg_duration, a gauge metric) → avg for the typical value over the window, or max for the worst bucket. Not sum — adding percentiles together is meaningless, and it is the common wrong choice.
• A current reading, where only the newest bucket matters → first.

{
  "kind": "query",
  "resultShape": "timeseries",
  "queries": [
    {
      "id": "q1",
      "name": "A",
      "enabled": true,
      "whereClause": "",
      "aggregation": "count",
      "stepInterval": "",
      "orderByDirection": "desc",
      "addOns": {
        "groupBy": false,
        "having": false,
        "orderBy": false,
        "limit": false,
        "legend": false
      },
      "groupBy": [],
      "having": "",
      "orderBy": "",
      "limit": "",
      "legend": "",
      "dataSource": "traces"
    }
  ],
  "transform": {
    "reduceToValue": {
      "field": "value",
      "aggregate": "sum"
    }
  }
}

The breakdown shape

resultShape: "breakdown" returns one row per group instead of a series over time — the shape pie, hbar, funnel and heatmap need. It requires a group-by, and limit caps the rows (honoured for 1–100).

{
  "kind": "query",
  "resultShape": "breakdown",
  "queries": [
    {
      "id": "q1",
      "name": "A",
      "enabled": true,
      "whereClause": "",
      "aggregation": "count",
      "stepInterval": "",
      "orderByDirection": "desc",
      "addOns": {
        "groupBy": true,
        "having": false,
        "orderBy": false,
        "limit": false,
        "legend": false
      },
      "groupBy": [
        "service.name"
      ],
      "having": "",
      "orderBy": "",
      "limit": "",
      "legend": "",
      "dataSource": "traces"
    }
  ],
  "limit": 10
}

A complete widget

The sections above describe add_dashboard_widget's parameters, which it assembles into a widget for you. update_dashboard_widget, replace_dashboard_widgets and dashboard_json take the assembled object instead — this is its shape. timeRange and sectionId/tabId are the only other top-level keys, both optional.

{
  "id": "w-error-rate",
  "visualization": "chart",
  "dataSource": {
    "kind": "query",
    "resultShape": "timeseries",
    "queries": [
      {
        "id": "q1",
        "name": "A",
        "enabled": true,
        "whereClause": "service.name = \"api\"",
        "aggregation": "error_rate",
        "stepInterval": "",
        "orderByDirection": "desc",
        "addOns": {
          "groupBy": true,
          "having": false,
          "orderBy": false,
          "limit": false,
          "legend": false
        },
        "groupBy": [
          "service.name"
        ],
        "having": "",
        "orderBy": "",
        "limit": "",
        "legend": "",
        "dataSource": "traces"
      }
    ]
  },
  "display": {
    "title": "Error rate by service",
    "chartId": "query-builder-line",
    "unit": "percent",
    "chartPresentation": {
      "legend": "visible"
    }
  },
  "layout": {
    "x": 0,
    "y": 0,
    "w": 4,
    "h": 6,
    "minW": 2,
    "minH": 2
  }
}

Units (display.unit)

The one that bites: Maple's percent tokens are inverted relative to Grafana's.

• percent expects a fraction 0–1 and multiplies by 100 on render. (Grafana calls this percentunit.)
• percent_100 expects 0–100 and renders as-is. (Grafana calls this one percent.)

The traces error_rate aggregation returns a 0–1 ratio, so it pairs with percent. Most exporter metrics named *_percent/*_utilization already report 0–100 and want percent_100. Getting it backwards renders 100× off with no error anywhere.

display.unit is stored as an open string, so an unrecognised value like "ms", "%" or "GB" saves successfully and then renders as a plain number. The write tools warn when they see one and suggest the right token. The same vocabulary applies to display.yAxis.unit, display.xAxis.unit and display.columns[].unit.

A gauge's arc is independent of its unit and defaults to 0–100: on a percent gauge set display.gauge: { "min": 0, "max": 1 } or the needle sits pinned at zero.

Queries

A query draft is discriminated on dataSource (traces / logs / metrics). The metric-only fields belong solely to metrics queries; do not add them to trace or log queries:

• metricName — required; discover real names with list_metrics.
• metricType — required, one of sum, gauge, histogram, exponential_histogram. Anything else fails to decode.
• signalSource — optional, one of default, meter. Omit it unless you know you need meter.
• isMonotonic — optional; false marks a Sum as an UpDownCounter, which changes the aggregations that make sense (rate/increase assume a monotonic counter).

addOns is required, and all five keys must be present

addOns: { groupBy, having, orderBy, limit, legend } — every key, every time. A missing one fails to decode. Each flag gates whether the matching field is read at all, which is why groupBy without addOns.groupBy: true silently does nothing.

Aggregations, per source

On traces only, setting valueField: "attr.<key>" switches the query to numeric-attribute mode, where the aggregation is one of avg, sum, min, max, p50, p95, p99. This is the only place a bare p50/p95/p99 is valid — latency percentiles are spelled p95_duration. Metrics never accept percentiles.

Group-by tokens

groupBy is ignored unless addOns.groupBy is true. This is the single most common silent failure: the array is present, the chart shows an ungrouped total, and nothing errors.

Anything outside the literal list must use a supported prefix; unrecognised tokens are dropped, which makes the write tools reject the widget rather than save a mis-scoped chart.

whereClause is a custom grammar, not SQL

Operators — the only ones: =, !=, >, <, >=, <=, contains, !contains, exists, !exists. Clauses join with AND; there is no OR and no parentheses. Values use double quotes. There is no IS NULL / IS NOT NULL — write <key> exists or <key> !exists. exists means present and non-empty, because attributes live in ClickHouse Map columns where a missing key reads back as ''.

On traces any bare key outside the structured allowlist (service.name, span.name, deployment.environment, vcs.ref.head.revision, root_only, has_error) is treated as a span attribute, so db.system = "clickhouse" works directly. Cap: 5 attr.* plus 5 resource.* filters per query.

Formulas and hidden series

formulas: [{ id, name, expression, legend }] references queries by name (A / B), and is valid on the timeseries shape only. Marking a query hidden: true is UI-only in raw JSON — also add transform.hideSeries.baseNames: ["A"] or the auxiliary series renders at full scale and flattens the axis.

Display config

Stored but not rendered

These decode and persist, and the chart renderer ignores them. Setting one to fix a problem will look like it worked and change nothing: yAxis.min, yAxis.max, every xAxis field, seriesMapping, colorOverrides, chartPresentation.fillNulls, gauge.style. To bound a chart's axis use yAxis.softMin/softMax.

Per-widget time range

A widget follows the dashboard's range unless it carries its own top-level timeRange: {"type":"relative","value":"30m"} or {"type":"absolute","startTime":"…","endTime":"…"}. Pin one only when the window is part of what the tile means. Because update_dashboard_widget replaces the whole widget, omitting timeRange there REMOVES an existing override.

Raw SQL widgets

Pass sql to add_dashboard_widget and the tool builds the data source for you. Call describe_warehouse_tables first — a hallucinated table or column silently produces an empty chart.

Macros

• $__orgFilter → required; scopes the query to your org.
• $__timeFilter(Column) → a bare column identifier, no expressions. Prefer this in WHERE.
• $__startTime / $__endTime → toDateTime(…) literals for use outside a WHERE comparison.
• $__interval_s → bucket size in seconds; only interpolate it if the SQL buckets time.

Conventions that catch everyone

• Columns are PascalCase (ServiceName, Timestamp) — never snake_case.
• StatusCode / SeverityText / SpanKind values are Title Case ('Error', not 'ERROR'). Wrong casing runs fine and matches zero rows.
• Span Duration is nanoseconds. Divide by 1e6 for ms.
• SpanAttributes['key'] — square brackets. A missing key returns '', not NULL.
• One statement only; writes are rejected; every query is wrapped in LIMIT 1001.

What to SELECT, per panel type

The renderer is opinionated. Wrong aliases give an empty chart or [object Object].

• line / area / bar — a DateTime bucket as the FIRST column (alias bucket) plus one or more numeric columns; each becomes a series named after the column. String columns are dropped, so multi-series must be pivoted in SQL with countIf(...) — tall form (bucket, ServiceName, count()) collapses to one aggregate line.
• stat / gauge — one scalar aliased value.
• pie / funnel / hbar — a string column aliased name plus a numeric column. Cap at ~8–10 rows.
• heatmap — three columns aliased x, y, value; string-cast numeric x/y.
• histogram — one numeric column aliased value, one row per observation; add LIMIT 5000.
• table — any rows; columns render in order, so use AS for readable headers.
• list — not supported. A list is configured by display.listDataSource.

SELECT toStartOfInterval(Timestamp, INTERVAL $__interval_s SECOND) AS bucket,
       countIf(SeverityText = 'Error') AS Error,
       countIf(SeverityText = 'Warn')  AS Warn
FROM logs
WHERE $__orgFilter AND $__timeFilter(Timestamp)
GROUP BY bucket
ORDER BY bucket

granularity_seconds only matters if the SQL references $__interval_s. Either use toStartOfInterval(…, INTERVAL $__interval_s SECOND) with it, or a fixed toStartOf* without it — mixing them means the setting silently does nothing.