RRepoGEO

REPOGEO REPORT · LITE

maquina-app/rails-mcp-server

Default branch main · commit 8536f2c1 · scanned 6/6/2026, 11:21:28 PM

GitHub: 557 stars · 35 forks

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 maquina-app/rails-mcp-server, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Refine the README's opening sentence to emphasize specialized Rails analysis

    Why:

    CURRENT
    A Ruby implementation of a Model Context Protocol (MCP) server for Rails projects. This server allows LLMs (Large Language Models) to interact with Rails projects through the Model Context Protocol, providing capabilities for code analysis, exploration, and development assistance.
    COPY-PASTE FIX
    The **Rails MCP Server** is a specialized Ruby gem that provides a Model Context Protocol (MCP) backend, enabling Large Language Models (LLMs) to deeply analyze, explore, and assist with Ruby on Rails projects. It offers a structured interface for AI to understand and interact with your Rails codebase, routes, models, and environment.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://maquina.app

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 maquina-app/rails-mcp-server
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/openai-ruby
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/openai-ruby · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. googleapis/google-cloud-ruby · recommended 1×
  4. aws/aws-sdk-ruby · recommended 1×
  5. langchain-rb/langchain-rb · recommended 1×
  • CATEGORY QUERY
    How can I integrate AI models to analyze and assist with my Ruby on Rails project?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API (openai/openai-ruby)
    2. Hugging Face Transformers
    3. Google Cloud AI Platform / Vertex AI (googleapis/google-cloud-ruby)
    4. AWS SageMaker (aws/aws-sdk-ruby)
    5. LangChain.rb (langchain-rb/langchain-rb)
    6. TensorFlow.rb (tensorflow/tensorflow-ruby)
    7. PyTorch
    8. ONNX Runtime

    AI recommended 8 alternatives but never named maquina-app/rails-mcp-server. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow LLMs to explore Rails project structure and execute sandboxed code?
    you: not recommended
    AI recommended (in order):
    1. Smol AI
    2. Open Interpreter
    3. LangChain
    4. AutoGPT
    5. GPT Engineer

    AI recommended 5 alternatives but never named maquina-app/rails-mcp-server. 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 maquina-app/rails-mcp-server?
    pass
    AI named maquina-app/rails-mcp-server explicitly

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

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

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

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MARKDOWN (README)
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