schema-discovery

by microsoft

Explore Power BI semantic model schemas using DAX INFO functions. Progressive metadata discovery strategy, scope estimation, and narrowing techniques for…

npx skills add https://github.com/microsoft/fabric-apps-analytic-templates --skill schema-discovery

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Schema Discovery

Task	Reference	Notes
Must/Prefer/Avoid	SKILL.md: Must/Prefer/Avoid	Guardrails for schema discovery
Progressive Discovery Strategy	SKILL.md: Progressive Schema Discovery	Decision tree for on-demand metadata fetching
Recommended Discovery Order	SKILL.md: Recommended Discovery Order	Start with scope estimation → tables → columns → measures → relationships
Metadata Object → INFO Function Map	SKILL.md: Metadata Object → INFO Function Map	Tables, columns, measures, relationships, calc groups, calendars, UDFs, variations
Scope Estimation Queries	discovery-queries.md: Scope Estimation Queries	Probe table/column/measure/relationship counts before deep discovery
INFO Output Columns	discovery-queries.md: INFO Output Columns	INFO.VIEW.* (read access); INFO.* (may need elevated access)
Narrowing Results (Projection + Filtering)	discovery-queries.md: Narrowing Results	SELECTCOLUMNS + FILTER to reduce output volume
Advanced Metadata (Calc Groups, Calendars, UDFs)	discovery-queries.md: Advanced Metadata Queries	INFO.CALCULATIONGROUPS, INFO.CALENDARS, INFO.USERDEFINEDFUNCTIONS, INFO.VARIATIONS
Complete INFO Function Catalog	discovery-queries.md: Complete INFO Function Catalog	Dynamic query to enumerate all INFO functions in the engine

Must / Prefer / Avoid

Must

Use fully-qualified 'Table'[Column] for column references
Use simple [Measure] for measure references

Prefer

INFO.VIEW functions for initial metadata discovery (read access, lightweight)
Progressive schema discovery over full schema dumps
SELECTCOLUMNS + FILTER to narrow INFO results

Avoid

Fetching full schema upfront — discover incrementally based on need
Re-fetching metadata already discovered in this conversation
Using GetSemanticModelSchema or GenerateQuery MCP tools (these are not available)
Using DiscoverArtifacts to find a semantic model — that MCP tool is not available; use npx fabric-app-data search (see fabric-cli) instead

Progressive Schema Discovery

Discover metadata incrementally based on what the user actually needs.

Strategy (Decision Tree)

User asks a question
  → Do I know which tables are relevant?
    → NO: Run INFO.VIEW.TABLES() to get table inventory
    → YES: Do I know the columns/measures for those tables?
      → NO: Run filtered INFO.VIEW.COLUMNS() and INFO.VIEW.MEASURES() for those tables
      → YES: Do I need relationships?
        → YES: Run INFO.VIEW.RELATIONSHIPS() filtered to those tables
        → NO: Do I need advanced metadata?
          → Have elevated INFO functions already failed in this session?
            → YES: Skip — assume no permission for all elevated queries
            → NO: Try the relevant elevated query:
              → Calculation groups: INFO.CALCULATIONGROUPS() + INFO.CALCULATIONITEMS()
              → Calendars: INFO.CALENDARS()
              → User-defined functions: INFO.USERDEFINEDFUNCTIONS()
              → Variations: INFO.VARIATIONS()
              → If query fails with permission error: mark elevated access as unavailable
  → Use discovered schema to write DAX queries (see dax-authoring skill)

Rules

Start with scope estimation — Run the scope probe query first to understand model size
Discover on demand — Only fetch tables/columns/measures relevant to the current user request
Use INFO.VIEW functions first (read access) — tables, columns, measures, relationships
Use INFO functions (may need elevated access) for: calculation groups, calculation items, variations, user-defined functions, calendars
Handle permission failures gracefully — If an elevated INFO function fails but INFO.VIEW.* functions succeeded, assume the user lacks elevated permissions and skip all elevated discoveries entirely
Always narrow results — Use SELECTCOLUMNS + FILTER to fetch only needed columns and rows
Cache discovered schema mentally — Don't re-fetch what you've already discovered in this conversation

Recommended Discovery Order

Scope estimation — Count tables, columns, measures, relationships
Tables — Get table names, identify relevant ones
Columns — Get columns for relevant tables only
Measures — Get measures (prefer using these over raw aggregations)
Relationships — Get relationships between relevant tables
Advanced metadata (if needed) — Calculation groups, calendars, UDFs, variations

Scope Estimation Query

EVALUATE
ROW(
    "TableCount", COUNTROWS(INFO.VIEW.TABLES()),
    "ColumnCount", COUNTROWS(INFO.VIEW.COLUMNS()),
    "MeasureCount", COUNTROWS(INFO.VIEW.MEASURES()),
    "RelationshipCount", COUNTROWS(INFO.VIEW.RELATIONSHIPS())
)

Metadata Object → INFO Function Map

Metadata Object	Primary INFO Functions	Access Level
Tables	`INFO.VIEW.TABLES()`	Read
Columns	`INFO.VIEW.COLUMNS()`	Read
Measures	`INFO.VIEW.MEASURES()`	Read
Relationships	`INFO.VIEW.RELATIONSHIPS()`	Read
Model config	`INFO.MODEL()`	May need elevated
Calculation groups	`INFO.CALCULATIONGROUPS()`, `INFO.CALCULATIONITEMS()`	May need elevated
Calendars	`INFO.CALENDARS()`, `INFO.CALENDARCOLUMNGROUPS()`, `INFO.CALENDARCOLUMNREFERENCES()`	May need elevated
User-defined functions	`INFO.USERDEFINEDFUNCTIONS()`	May need elevated
Variations	`INFO.VARIATIONS()`	May need elevated

For the full query catalog and output column details, see discovery-queries.md.

Running Discovery Queries

Use npx fabric-app-data query <alias> --query '<DAX>' to execute INFO queries against a semantic model. For full CLI options (profiles, file input, result limits), see the fabric-cli skill.

npx fabric-app-data query <alias> --query "EVALUATE INFO.VIEW.TABLES()"

Troubleshooting

Problem	Solution
INFO functions return permission errors	Fall back to INFO.VIEW functions; mark elevated access as unavailable for this session
Metadata output too large	Use scope estimation + narrowing patterns from discovery-queries.md