R1 Dash Master

An MCP server that builds importable RUCKUS One Data Studio dashboards from a simple declarative spec. Output is a .zip you import via Data Studio → Settings → Import Dashboard. Pure offline generation — no R1 API credentials needed.

Data Studio is Apache Superset on an Apache Druid backend (deployment: ALTO). This tool encodes the reverse-engineered dataset catalog and the chart/query grammar so you (or an agent) can build valid dashboards without learning Superset internals or guessing field names.

Tools

• list_datasets() — all 18 R1 datasets (internal name, cube name, id, counts).

• describe_dataset(name) — exact metric + dimension names for one dataset.

• validate_spec(spec) — check a spec against the catalog before building.

• build_dashboard(spec, filename?) — emit an importable .zip (written to out/).

Related MCP server: Query Analytics

Spec format

{
  "title": "Network Intelligence",      // generic — NEVER tenant-specific (bundles are portable across ECs)
  // tenant_id: OPTIONAL — omit it. Import auto-rescopes to the target EC (tenant). Only include to hard-pin a tenant.
  "time_range": "Last week",            // default for all charts
  "rows": [                              // each row = list of charts; widths in a row sum to <= 12
    [ {chart}, {chart} ]
  ]
}

Chart:

{
  "type": "bignum" | "bignum_trend" | "line" | "bar" | "area" | "scatter" | "pie" | "table"
         | "gauge" | "heatmap" | "funnel" | "pivot" | "mixed" | "tree" | "bubble",
  "stacked": true,                       // bar/area only: stack the series
  "x": "apMac",                          // line/bar/area/scatter: optional DIMENSION x-axis (default __time)
  // pivot:  "rows": ["zoneName"], "columns": ["radio"], "metrics": [...]
  // mixed:  "metrics": [...] (bars) + "metrics_b": [...] (line) + optional "groupby"/"groupby_b","format_b"
  // tree:   "id": "apName", "parent": "apModel", "name": "apName", "metric": "..."
  // bubble: "entity": "apName", "x": <metric>, "y": <metric>, "size": <metric>  (x/y/size are METRICS here)
  // funnel/gauge/heatmap: "metric" (singular) + "groupby" ([dim]; heatmap uses first dim as Y)
  "dataset": "binnedSessions",          // internal name from list_datasets
  "title": "...", "width": 1-12,
  "metric":  "User Traffic (Total)"     // bignum/pie; string = saved metric
           | {"sql": "1.0*SUM(a)/SUM(b)", "label": "Rate"},  // or custom-SQL (ratios/%)
  "metrics": [ ... ],                    // line/table (list of the same forms)
  "groupby": ["radio"],
  "filter":  ["radio","5"]  | [["radio","5"],["zoneName","X"]],
  "time_range": "Last day",              // optional per-chart override
  "format": ".1%",                       // d3 number format (rates -> ".1%")
  "percent_of_total": ["Traffic (Total)"], // table: share-of-column-total column
  "row_limit": 25
}

Layout & cross-filtering (design convention)

Data Studio dashboards are cross-filterable: clicking a value in any chart (e.g. a venue in a venue table) filters the entire dashboard to that value; clearing it up top removes the filter. So put venue and AP tables/charts near the TOP — they double as interactive filter controls. Recommended order: KPI row → venue (and AP) table → detail charts below. The builder preserves row order from the spec, so order your rows that way.

Grammar notes baked in (gotchas)

• Field names are exact & dataset-specific. radio not Radio; Unique Client MAC Count not "Unique Client Count"; User Traffic(Total) (no space) in sessionsSummary vs User Traffic (Total) (space) in binnedSessions. validate_spec catches saved-metric/dim typos.

• Custom-SQL metrics reference RAW columns (e.g. successCount), not display metric names, and integer division floors — always 1.0 * (or 100.0 *). See raw_columns in the catalog.

• Rate vs share: a true rate = SQL metric + .1% format. percent_of_total (table percent_metrics) means "% of the column total" (contribution), not "format as %".

• Dashboards are transmutable across ECs — keep titles generic, swap tenant_id.

CLI (without MCP)

python3 builder.py examples/network_intelligence.json out/network_intelligence_IMPORT.zip

Run as MCP

pip install -r requirements.txt
python3 server.py

Then register in your MCP client config (command: python3, args: ["/home/alek/src/r1_dash_master/server.py"]).

Examples vs. Gallery

• examples/*.json — source specs, for driving the MCP/builder and learning the spec format.

• gallery/*.zip — prebuilt, ready-to-import dashboards. Since bundles are tenant-less, they auto-rescope to whatever EC you import them into. Grab one → Data Studio → Settings → Import Dashboard. Current set: executive_overview, capacity_rf, connection_health, network_intelligence, switch_health, chart_gallery.

• Regenerate the gallery from specs anytime: ./build_gallery.sh (keeps zips in sync).

Status

Catalog: 18/19 datasets mapped (AP Alarms & Controller Inventory are SmartZone-only, N/A in R1). Viz (15): bignum, bignum_trend, line, bar, area, scatter, pie, table, gauge, heatmap, funnel, pivot, mixed, tree, bubble. Query grammar: saved + custom-SQL metrics, percent-of-total, dimension + time filters, d3 formats. Cross-filtering is built in (click a chart value to filter the dashboard). Not yet: explicit dashboard-level native filter bar; remaining viz (treemap, sunburst, box plot, radar, waterfall, graph, histogram, calendar heatmap, sankey, smooth/stepped line); auto-import (needs an analytics-backend API — import the zip via UI).