RRepoGEO

REPOGEO REPORT · LITE

modelcontextprotocol/quickstart-resources

Default branch main · commit 36b6ca40 · scanned 5/19/2026, 2:57:02 AM

GitHub: 1,094 stars · 616 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 modelcontextprotocol/quickstart-resources, 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 opening to specify category

    Why:

    CURRENT
    A repository of servers and clients from the following Model Context Protocol tutorials:
    COPY-PASTE FIX
    Quickstart resources for the Model Context Protocol (MCP), an open, decentralized standard for verifiable AI model context. This repository provides example servers and clients to help developers quickly integrate custom LLM applications with MCP.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai, llm, protocol, context, verifiable-ai, decentralized, quickstart, examples, tutorials
  • mediumreadme#3
    Clarify existing license in README

    Why:

    COPY-PASTE FIX
    Add a section like: "## License\nThis project is licensed under [describe the license, e.g., 'a custom license detailed in the LICENSE file' or 'the specific terms outlined in the LICENSE file']. Please refer to the [LICENSE](./LICENSE) file for full details."

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 modelcontextprotocol/quickstart-resources
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. Haystack · recommended 1×
  4. OpenAI API · recommended 1×
  5. Microsoft Semantic Kernel · recommended 1×
  • CATEGORY QUERY
    How to connect a custom LLM application to a standardized context protocol?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI API
    5. Microsoft Semantic Kernel
    6. Hugging Face Transformers
    7. Custom API Gateway/Microservice

    AI recommended 7 alternatives but never named modelcontextprotocol/quickstart-resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework simplifies creating a server for sharing AI model context data?
    you: not recommended
    AI recommended (in order):
    1. FastAPI (tiangolo/fastapi)
    2. Pydantic (pydantic/pydantic)
    3. Flask (pallets/flask)
    4. Marshmallow (marshmallow-code/marshmallow)
    5. Django REST Framework (encode/django-rest-framework)
    6. Node.js (nodejs/node)
    7. Express.js (expressjs/express)
    8. Joi (hapijs/joi)
    9. Zod (colinhacks/zod)
    10. Swagger-UI-Express (scottie1984/swagger-ui-express)
    11. Go (golang/go)
    12. Gin Gonic (gin-gonic/gin)

    AI recommended 12 alternatives but never named modelcontextprotocol/quickstart-resources. 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 modelcontextprotocol/quickstart-resources?
    pass
    AI named modelcontextprotocol/quickstart-resources explicitly

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

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

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

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modelcontextprotocol/quickstart-resources — 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