schema-discovery

Schema Discovery

Table of Contents

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

1. Scope estimation — Count tables, columns, measures, relationships
2. Tables — Get table names, identify relevant ones
3. Columns — Get columns for relevant tables only
4. Measures — Get measures (prefer using these over raw aggregations)
5. Relationships — Get relationships between relevant tables
6. 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