Fabric Data — Connections and Runtime Queries

Use this skill when you need to wire a Power BI semantic model, inspect schema, quick-test DAX, or handle query results in the app.

Fast path

1. Confirm the connection alias in fabric.yaml.
2. If no alias exists, add one:

   npx fabric-app-data add <alias> --from-url "<Fabric portal URL>"
   # or, when you have IDs:
   npx fabric-app-data add <alias> -w <workspaceId> -i <itemId>

   Use workspaceId: "me" for My Workspace. The long flags are --workspace and --item; do not use --workspace-id or --item-id.
3. Regenerate the config:

   npx fabric-app-data generate -o src/fabric.generated.ts

4. Discover schema or test DAX with the same pipeline the app uses:

   npx fabric-app-data query <alias> --query "EVALUATE INFO.VIEW.TABLES()"
   npx fabric-app-data query <alias> --file src/queries/hero.dax --limit 100

5. In app code, use the template's query hook; do not hand-edit generated config.

If you only have the workspace + item (dataset) id of the model, wire it with fabric-app-data add <alias> -w <workspaceId> -i <itemId>.

CLI essentials

All commands except init walk up from the current directory until they find fabric.yaml; run init from the project root.

query options:

• -q, --query <query> — inline DAX.
• -f, --file <path> — .dax file.
• -l, --limit <rows> — max returned rows; default 1000, max 1000.
• -p, --profile <name> — profile; defaults to activeProfile.

The CLI caps output at 1000 rows. Trimmed JSON includes _cliWarning; refine the DAX with filters or aggregation instead of trying to pull more rows for a visual.

Feeds the headless preview. A saved query result (… --file q.dax > rows.json) can be piped straight into npm run preview -- --spec s.json --data rows.json to render a chart against this live data, or the preview script can run the query itself (--query <alias> --dax-file q.dax). See the headless-preview skill.

fabric.yaml

activeProfile: dev
profiles:
  dev:
    semanticModels:
      sales:
        workspaceId: "00c98f7c-..."
        itemId: "03f2dc11-..."
      myModel:
        workspaceId: "me"
        itemId: "c078a68d-..."
  production:
    semanticModels:
      sales:
        workspaceId: "aabbccdd-..."
        itemId: "11223344-..."

Supported URL segments include semanticmodels, modeling, lakehouses, and warehouses; semantic-model dashboard work usually needs semanticModels.

Generated contract

fabric.generated.ts is auto-generated from the active profile and exports a plain config object:

export const fabricConfig = {
  semanticModels: {
    sales: {
      workspaceId: "00c98f7c-...",
      itemId: "03f2dc11-...",
    },
  },
} as const;

Edit fabric.yaml, then regenerate. Never hand-edit src/fabric.generated.ts.

Runtime querying

The template already wires FabricClient and exposes useSemanticModelQuery; query through that hook unless you are changing plumbing.

const result = useSemanticModelQuery({ connection, query });

const rows = toChartData(result.data, {
  columns: { Category: "Product[Category]", Revenue: "Revenue" },
});

Direct SDK shape, when needed:

import { FabricClient } from "@microsoft/fabric-app-data";

const client = new FabricClient({ proxy, ...fabricConfig });
const model = client.semanticModel("sales");
const result = await model.query("EVALUATE SUMMARIZECOLUMNS('Product'[Category], \"Revenue\", [Revenue])");

if (result.status === "success") {
  // result.table.columns -> [{ name: "Product[Category]", dataType: "String" }, { name: "Revenue", dataType: "Double" }]
  // result.table.rows    -> [["Bikes", 12345], ["Accessories", 6789]]
} else {
  console.error(result.error.category, result.error.message);
}

Result rules agents must remember

• Always check result.status when using the SDK result directly; queries never throw.
• Successful queries return one table: table.columns metadata and table.rows as unknown[][].
• Rows are positional: rows[i][j] matches columns[j]. Map deliberately before charting.
• Error categories: query (bad DAX), overflow (unsafe numeric value), api (HTTP/Fabric), network, unknown.
• Date/DateTime values are strings like "2024-01-15T10:30:00.000" with no timezone suffix. Treat them as timezone-unaware model values.
• Integers outside Number.MAX_SAFE_INTEGER return category: "overflow" instead of losing precision.

Caching

Query results are cached in memory by default (LRU, 64 entries). Successes and DAX query errors are cached; transient api, network, and unknown errors are not.

await model.query(dax);                         // cached on repeat
await model.query(dax, { bypassCache: true });  // force fresh
model.clearCache();                             // this model
client.clearCache();                            // all models

Disable or tune only when you are changing data plumbing:

new FabricClient({ proxy, ...fabricConfig, cache: { enabled: true, maxEntries: 64 } });

Minimal types

type WorkspaceId = "me" | (string & {});
interface FabricItemRef { workspaceId: WorkspaceId; itemId: string }
interface QueryColumn { name: string; dataType: string }
interface QueryTable { columns: QueryColumn[]; rows: unknown[][] }
interface QueryError { category: "api" | "query" | "overflow" | "network" | "unknown"; message: string; code?: string; details?: string }
type QueryResult =
  | { status: "success"; table: QueryTable; requestId: string }
  | { status: "error"; error: QueryError; requestId: string };
type CachedQueryResult = QueryResult & { fromCache: boolean; cachedAt?: Date };