RRepoGEO

REPOGEO REPORT · LITE

google/mcp

Default branch main · commit 888c4464 · scanned 5/18/2026, 7:33:28 PM

GitHub: 4,093 stars · 460 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
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 google/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
    Update README H1 to explicitly state AI context purpose

    Why:

    CURRENT
    # `google/mcp`
    COPY-PASTE FIX
    # `google/mcp`: Model Context Protocol (MCP) for AI Grounding
  • hightopics#2
    Add specific topics for AI context and grounding

    Why:

    COPY-PASTE FIX
    ai, llm, r-a-g, retrieval-augmented-generation, model-grounding, context-servers, google-cloud, machine-learning
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [Insert official project homepage URL here, e.g., a Google Cloud documentation page or project site]

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 google/mcp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Databricks Lakehouse Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Databricks Lakehouse Platform · recommended 1×
  2. Confluent Platform · recommended 1×
  3. Snowflake Data Cloud · recommended 1×
  4. Google Cloud Platform (GCP) · recommended 1×
  5. Google Cloud Pub/Sub · recommended 1×
  • CATEGORY QUERY
    How to connect diverse enterprise data sources to provide real-time context for AI models?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Confluent Platform
    3. Snowflake Data Cloud
    4. Google Cloud Platform (GCP)
    5. Google Cloud Pub/Sub
    6. Google Cloud Dataflow
    7. Google BigQuery
    8. Amazon Web Services (AWS)
    9. Amazon Kinesis
    10. AWS Glue
    11. Amazon SageMaker Feature Store
    12. Microsoft Azure
    13. Azure Event Hubs
    14. Azure Stream Analytics
    15. Azure Synapse Analytics
    16. Palantir Foundry

    AI recommended 16 alternatives but never named google/mcp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for deploying scalable context servers for AI model grounding on cloud platforms?
    you: not recommended
    AI recommended (in order):
    1. Redis Enterprise
    2. Amazon DynamoDB
    3. Google Cloud Firestore
    4. Azure Cosmos DB
    5. Elasticsearch
    6. Milvus
    7. Pinecone

    AI recommended 7 alternatives but never named google/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 google/mcp?
    pass
    AI named google/mcp explicitly

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

  • If a team adopts google/mcp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google/mcp 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 google/mcp solve, and who is the primary audience?
    pass
    AI named google/mcp explicitly

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

Embed your GEO score

Drop this badge into the README of google/mcp. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/google/mcp"><img src="https://repogeo.com/badge/google/mcp.svg" alt="RepoGEO" /></a>
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google/mcp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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google/mcp — RepoGEO report