RRepoGEO

REPOGEO REPORT · LITE

google/mcp

Default branch main · commit 56815c6e · scanned 6/30/2026, 2:02:47 AM

GitHub: 4,270 stars · 485 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
    Reposition README H1 to clearly state "Model Context Protocol" and its purpose for LLMs

    Why:

    CURRENT
    # `google/mcp`
    COPY-PASTE FIX
    # `google/mcp`: Model Context Protocol (MCP) Servers for LLM Integration
  • hightopics#2
    Add relevant topics to improve category visibility

    Why:

    COPY-PASTE FIX
    model-context-protocol, mcp, llm, ai-context, google-cloud, api-integration, database-integration, open-source-servers
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://cloud.google.com/

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
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI Functions · recommended 1×
  4. psycopg2 · recommended 1×
  5. SQLAlchemy · recommended 1×
  • CATEGORY QUERY
    How to integrate large language models with various cloud databases and APIs effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Functions
    4. psycopg2
    5. SQLAlchemy
    6. TypeORM
    7. Prisma
    8. pymongo
    9. boto3
    10. google-cloud-firestore
    11. requests
    12. axios
    13. node-fetch
    14. google-cloud-sdk
    15. azure-sdk-for-python
    16. Zapier NLA
    17. Make
    18. Microsoft Semantic Kernel

    AI recommended 18 alternatives but never named google/mcp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source solutions for deploying AI context management servers in a cloud environment.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Weaviate (weaviate/weaviate)
    4. Qdrant (qdrant/qdrant)
    5. Pinecone
    6. Milvus (milvus-io/milvus)
    7. Redis (redis/redis)

    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

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google/mcp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite