RRepoGEO

REPOGEO REPORT · LITE

golf-mcp/golf

Default branch main · commit df0004b8 · scanned 5/31/2026, 12:52:24 AM

GitHub: 829 stars · 68 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 golf-mcp/golf, 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
    Clarify 'MCP' and reposition the README H3 to explicitly state AI agent backend framework

    Why:

    CURRENT
    <h3 align="center">Easiest framework for building MCP servers</h3>
    COPY-PASTE FIX
    <h3 align="center">Easiest framework for building AI Agent backends (Multi-Agent Communication Protocol servers)</h3>
  • mediumtopics#2
    Add more specific AI agent backend/server topics and clarify 'MCP'

    Why:

    CURRENT
    agent-runtime, ai, ai-agent, ai-agent-tools, ai-agents, ai-platform, aiagents, auth, authentication, authorization, mcp, mcp-framework, mcp-server, mcp-servers, mcp-tools, mcps, observability, python
    COPY-PASTE FIX
    agent-runtime, ai, ai-agent, ai-agent-tools, ai-agents, ai-platform, aiagents, auth, authentication, authorization, multi-agent-communication-protocol, mcp-server-framework, ai-agent-backend, ai-agent-orchestration, observability, python
  • lowreadme#3
    Add a sentence to the README overview explicitly positioning Golf as an AI agent backend framework

    Why:

    CURRENT
    Golf is a **framework** designed to streamline the creation of MCP server applications. It allows developers to define server's capabilities—*tools*, *prompts*, and *resources*—as simple Python files within a conventional directory structure. Golf then automatically discovers, parses, and compiles these components into a runnable MCP server, minimizing boilerplate and accelerating development.
    COPY-PASTE FIX
    Golf is a **framework** designed to streamline the creation of **AI agent backend applications (Multi-Agent Communication Protocol servers)**. It allows developers to define an agent's capabilities—*tools*, *prompts*, and *resources*—as simple Python files within a conventional directory structure. Golf then automatically discovers, parses, and compiles these components into a runnable, production-ready AI agent server, minimizing boilerplate and accelerating development.

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 golf-mcp/golf
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FastAPI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. FastAPI · recommended 2×
  2. Pydantic · recommended 2×
  3. Prometheus · recommended 2×
  4. Grafana · recommended 2×
  5. Kubernetes · recommended 1×
  • CATEGORY QUERY
    How to build a production-ready, secure, and scalable server framework for AI agents?
    you: not recommended
    AI recommended (in order):
    1. FastAPI
    2. Pydantic
    3. Kubernetes
    4. Docker
    5. Docker Compose
    6. PostgreSQL
    7. pgvector
    8. Pinecone
    9. Weaviate
    10. Qdrant
    11. Redis
    12. Nginx
    13. Traefik
    14. Envoy Proxy
    15. Prometheus
    16. Grafana

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

    Show full AI answer
  • CATEGORY QUERY
    Python framework for AI agent backends with built-in authentication, observability, and telemetry?
    you: not recommended
    AI recommended (in order):
    1. FastAPI
    2. Pydantic
    3. python-jose
    4. Authlib
    5. OpenTelemetry
    6. opentelemetry-instrumentation-fastapi
    7. opentelemetry-sdk
    8. Django REST Framework
    9. Django
    10. django-allauth
    11. Prometheus
    12. Grafana
    13. django-prometheus
    14. opentelemetry-instrumentation-django
    15. Celery
    16. Flask
    17. Flask-Login
    18. Flask-JWT-Extended
    19. opentelemetry-instrumentation-flask
    20. Sanic
    21. Sanic-JWT
    22. Litestar
    23. Litestar-Auth

    AI recommended 23 alternatives but never named golf-mcp/golf. 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 golf-mcp/golf?
    pass
    AI named golf-mcp/golf explicitly

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

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

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

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golf-mcp/golf — 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