RRepoGEO

REPOGEO REPORT · LITE

microsoft/powerbi-modeling-mcp

Default branch main · commit fe211b8e · scanned 6/2/2026, 3:47:26 PM

GitHub: 820 stars · 152 forks

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface microsoft/powerbi-modeling-mcp, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to clarify the project's nature

    Why:

    CURRENT
    The **Power BI Modeling MCP Server** implements the MCP specification to create a seamless connection between AI agents and Power BI semantic models. This project is in Public Preview and implementation may significantly change prior to our General Availability.
    COPY-PASTE FIX
    The **Power BI Modeling MCP Server** is a deployable application that provides a local API endpoint, enabling AI agents to programmatically interact with and modify Power BI semantic models. This project is in Public Preview and implementation may significantly change prior to our General Availability.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    power-bi, ai-agents, semantic-model, data-modeling, api, mcp, microsoft-fabric, low-code
  • mediumcomparison#3
    Add a comparison section to the README to clarify differentiation

    Why:

    COPY-PASTE FIX
    ## 🆚 How is this different from other tools?

The Power BI Modeling MCP Server is specifically designed for AI agents to interact with *Power BI semantic models*. It is not:
- A general-purpose data transformation tool like DBT or Apache Spark.
- A database migration or schema management tool like Liquibase or Flyway.
- A generic AI model or API like OpenAI's GPT-4 or Hugging Face models.
- An Object-Relational Mapper (ORM) like SQLAlchemy.

Instead, it provides a unique API layer for programmatic, agent-driven manipulation of Power BI models, enabling advanced automation and natural language interaction within the Power BI ecosystem.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface microsoft/powerbi-modeling-mcp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SQLAlchemy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SQLAlchemy · recommended 2×
  2. DBT (Data Build Tool) · recommended 1×
  3. OpenAI's GPT-4 · recommended 1×
  4. Hugging Face model · recommended 1×
  5. Liquibase · recommended 1×
  • CATEGORY QUERY
    How to integrate AI agents with data modeling for automated schema changes?
    you: not recommended
    AI recommended (in order):
    1. DBT (Data Build Tool)
    2. OpenAI's GPT-4
    3. Hugging Face model
    4. Liquibase
    5. Flyway
    6. Apache Atlas
    7. Claude 3
    8. SQLAlchemy
    9. JPA
    10. scikit-learn
    11. spaCy

    AI recommended 11 alternatives but never named microsoft/powerbi-modeling-mcp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    APIs for automated bulk updates and refactoring of data models?
    you: not recommended
    AI recommended (in order):
    1. Apache Kafka
    2. Kafka Streams
    3. ksqlDB
    4. Apache Flink
    5. Apache Spark
    6. Spark Streaming
    7. Structured Streaming
    8. AWS Glue
    9. Google Cloud Dataflow
    10. Apache Beam
    11. dbt
    12. SQLAlchemy
    13. JDBC

    AI recommended 13 alternatives but never named microsoft/powerbi-modeling-mcp. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of microsoft/powerbi-modeling-mcp?
    pass
    AI did not name microsoft/powerbi-modeling-mcp — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts microsoft/powerbi-modeling-mcp in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name microsoft/powerbi-modeling-mcp — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo microsoft/powerbi-modeling-mcp solve, and who is the primary audience?
    pass
    AI did not name microsoft/powerbi-modeling-mcp — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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