RRepoGEO

REPOGEO REPORT · LITE

marmotdata/marmot

Default branch main · commit 78db5286 · scanned 6/14/2026, 3:01:46 AM

GitHub: 576 stars · 19 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 marmotdata/marmot, 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
  • highabout#1
    Update 'about' description to explicitly state 'data catalog'

    Why:

    CURRENT
    The open-source context layer for your AI. Catalog your tables, topics, queues and APIs then expose real metadata to your AI agents.
    COPY-PASTE FIX
    Marmot: An open-source data catalog and context layer for AI agents. Catalog tables, topics, queues, and APIs to expose real metadata to your AI.
  • highreadme#2
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Why Marmot?
Unlike traditional data catalogs (e.g., OpenMetadata, Amundsen, DataHub) that often require extensive infrastructure and complex configuration, Marmot is designed for simplicity and speed. It ships as a **single binary** with an intuitive UI, making it easy to deploy and start cataloging in minutes. Marmot also uniquely focuses on being an **AI-ready context layer**, exposing real metadata to your AI agents through MCP, its API, and UI, a feature often missing or complex in other solutions.
  • mediumtopics#3
    Add AI-specific topics to improve query matching

    Why:

    CURRENT
    bigdata, data-catalog, data-collaboration, data-discovery, data-exploration, data-governance, data-lineage, data-observability, datacatalog, datadiscovery, dataengineering, lineage, mcp, mcp-server, metadata
    COPY-PASTE FIX
    bigdata, data-catalog, data-collaboration, data-discovery, data-exploration, data-governance, data-lineage, data-observability, datacatalog, datadiscovery, dataengineering, lineage, mcp, mcp-server, metadata, ai-agents, llm-tools

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 marmotdata/marmot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenMetadata
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenMetadata · recommended 2×
  2. Amundsen · recommended 1×
  3. DataHub · recommended 1×
  4. Apache Atlas · recommended 1×
  5. CKAN · recommended 1×
  • CATEGORY QUERY
    What's an open-source data catalog for AI agents that's easy to deploy?
    you: not recommended
    AI recommended (in order):
    1. Amundsen
    2. DataHub
    3. OpenMetadata
    4. Apache Atlas
    5. CKAN

    AI recommended 5 alternatives but never named marmotdata/marmot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to catalog data assets, manage metadata, and visualize data lineage for team collaboration?
    you: not recommended
    AI recommended (in order):
    1. Atlan
    2. Collibra Data Governance Center
    3. Alation Data Catalog
    4. Microsoft Purview
    5. Data.world
    6. OpenMetadata
    7. Informatica Enterprise Data Catalog

    AI recommended 7 alternatives but never named marmotdata/marmot. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 marmotdata/marmot?
    pass
    AI named marmotdata/marmot explicitly

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

  • If a team adopts marmotdata/marmot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named marmotdata/marmot explicitly

    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 marmotdata/marmot solve, and who is the primary audience?
    pass
    AI named marmotdata/marmot explicitly

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

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