| name | power-bi-ai-readiness |
| description | Score and prepare Power BI semantic models for AI (Fabric Data Agent / Copilot). Runs 11 checks covering star schema, naming, descriptions, synonyms, implicit measures, duplicates, date ambiguity, hidden objects, model complexity, Prep for AI config, and best practice analysis. Produces a 0-100 scorecard with AI Ready / Mostly Ready / Needs Improvement / Not Ready rating plus prioritized fix list. Use when the user mentions AI readiness, Prep for AI, data agent preparation, Copilot optimization, semantic model scoring, or making a model AI-ready. |
Semantic Model AI Readiness Analyzer
Score a Power BI semantic model on a 0–100 scale and produce a prioritized action plan to make it AI-ready for Fabric Data Agent and Power BI Copilot.
When to Use
- User asks to score, audit, or review a model for AI readiness
- User wants to prepare a semantic model for Fabric Data Agent or Copilot
- User mentions "Prep for AI", "AI data schema", "verified answers", or "AI instructions"
- User asks about making a model AI-ready or improving Copilot accuracy
Trigger phrases: "AI readiness", "score my model", "prep for AI", "data agent", "Copilot optimization", "AI-ready", "semantic model scoring", "AI data schema"
Prerequisites
Required Tools
- Power BI Modeling MCP Server — connect to the model and read metadata
connection_operations, model_operations, table_operations, column_operations, measure_operations, relationship_operations
Optional Tools
- Microsoft Learn MCP Server — look up latest Prep for AI documentation
Scoring Framework
| # | Check | Max Pts | What it evaluates |
|---|
| 1 | Star Schema & Relationships | 15 | M:M relationships, bidirectional cross-filters, isolated tables |
| 2 | Business-Friendly Naming | 10 | DIM_/FACT_ prefixes, _AMT/_QTY suffixes, all-caps names |
| 3 | Object Descriptions | 15 | % of tables, columns, measures with descriptions |
| 4 | Synonyms | 5 | Synonym coverage on tables, columns, measures |
| 5 | Implicit Measures | 10 | Numeric columns with SummarizeBy != None |
| 6 | Duplicate / Overlapping Measures | 5 | Near-identical measure names after normalization |
| 7 | Ambiguous Date Fields | 5 | Multiple visible date columns without guidance |
| 8 | Hidden Objects Risk | 5 | Hidden columns without descriptions (Verified Answers risk) |
| 9 | Model Complexity / Bloat | 5 | Visible helper measures, high column/measure counts |
| 10 | Prep for AI Configuration | 15 | AI Data Schema, AI Instructions, Verified Answers annotations |
| 11 | Best Practice Analyzer | 10 | String keys, missing format strings, redundant CALCULATE, etc. |
| Total | | 100 | |
Rating Scale
| Score | Rating | Meaning |
|---|
| 90-100 | AI READY | Well-optimized. Monitor and iterate. |
| 70-89 | MOSTLY READY | Good foundation — address flagged items. |
| 50-69 | NEEDS IMPROVEMENT | Address critical items before production. |
| 0-49 | NOT READY | Significant improvements needed. |
Workflow
Step 1: Connect to the Model
1. connection_operations(operation: "ListConnections")
2. If none: connection_operations(operation: "ListLocalInstances") or ConnectFabric
3. model_operations(operation: "Get") — confirm model name
Step 2: Gather Model Metadata
Run these in parallel where possible:
- table_operations(operation: "List")
- relationship_operations(operation: "List")
- measure_operations(operation: "List")
- column_operations(operation: "List") for each table
Filter out system tables: names starting with DateTableTemplate_ or LocalDateTable_.
Step 3: Run All 11 Checks
For each check, calculate the score and collect issues. See reference files for detailed logic:
Step 4: Produce the Scorecard
Output format:
══════════════════════════════════════════════════════════
SEMANTIC MODEL AI READINESS SCORECARD
Model: {model_name}
══════════════════════════════════════════════════════════
Check Score Progress
────────────────────────────────────────────────────────
Star Schema Validation 15/15 [####################] 100% PASS
Business-Friendly Naming 8/10 [################....] 80% PASS
Object Descriptions 10/15 [#############.......] 67% WARN
...
────────────────────────────────────────────────────────
TOTAL SCORE 72/100 (72%)
Rating : MOSTLY READY
Summary: Good foundation — address flagged items to maximize accuracy.
KEY ISSUES TO ADDRESS:
- CRITICAL: 45 objects missing descriptions
- WARNING: No synonyms configured
- CRITICAL: AI Instructions not detected
Step 5: Generate Prioritized Fix Plan
Classify issues into three tiers:
- Critical (blocks AI accuracy): Missing Prep for AI config, no descriptions, implicit measures
- Important (reduces quality): Technical naming, no synonyms, duplicate measures
- Recommended (polish): Hidden object descriptions, date guidance, helper measures
Step 6: Offer to Fix Issues
After presenting the scorecard, offer to remediate using MCP tools:
- Add descriptions:
column_operations(operation: "Update"), measure_operations(operation: "Update")
- Rename objects:
table_operations(operation: "Update"), column_operations(operation: "Update")
- Hide columns:
column_operations(operation: "Update", definitions: [{isHidden: true}])
- Disable implicit measures: Set SummarizeBy to None via column update
- Add synonyms: Use
column_operations / measure_operations to add synonyms
Bonus: Measure Dependency Analysis
When configuring AI Data Schema, it is critical to include all dependent objects. For each key measure, trace:
- Direct column references:
Table[Column]
- Measure-to-measure references:
[Other Measure]
Output a dependency matrix so the user knows exactly which tables, columns, and measures must be included in the AI Data Schema.
Key Resources
