RRepoGEO

REPOGEO REPORT · LITE

bgauryy/octocode-mcp

Default branch main · commit 40716767 · scanned 6/17/2026, 2:42:02 PM

GitHub: 863 stars · 73 forks

AI VISIBILITY SCORE
33 /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
2 / 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 bgauryy/octocode-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
    Clarify core function as an MCP server for AI assistants in README's opening

    Why:

    CURRENT
    Stop guessing. Octocode researches code locally and externally: your own workspace (ripgrep + LSP-level go-to-definition, references, call hierarchy) and the world's (GitHub repos, PRs, npm/PyPI packages), turning it into verifiable evidence your AI can search, read, and trace. Use it as an MCP server inside your AI assistant, or as a terminal CLI.
    COPY-PASTE FIX
    Octocode is an **MCP server** and **terminal CLI** that empowers AI assistants to perform deep, verifiable code research across your local workspace and the world's public repositories. It transforms code into AI-optimized knowledge, enabling semantic search and context generation for LLMs.
  • highreadme#2
    Add a clear statement about active maintenance status

    Why:

    COPY-PASTE FIX
    Octocode is an actively developed open-source project, maintained by bgauryy.
  • mediumreadme#3
    Add a dedicated 'What Octocode Solves' section

    Why:

    COPY-PASTE FIX
    ## What Octocode Solves
    AI assistants often struggle with deep, verifiable code research across diverse codebases. Octocode provides the missing link:
    - **Semantic Code Search:** Naturally search across public and private repositories based on your permissions.
    - **AI-Optimized Knowledge:** Transform any accessible codebase into AI-optimized knowledge for simple and complex flows.
    - **Real-time Context:** Find real implementations and live documentation from anywhere, feeding precise context to your LLM.

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 bgauryy/octocode-mcp
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. GitHub Copilot Chat · recommended 1×
  3. Sourcegraph Cody · recommended 1×
  4. OpenAI Custom GPTs · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I semantically search across multiple codebases using an AI assistant?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Chat
    2. Sourcegraph Cody
    3. OpenAI Custom GPTs
    4. LlamaIndex (run-llama/llama_index)
    5. LangChain (langchain-ai/langchain)
    6. Pinecone
    7. Weaviate (weaviate/weaviate)
    8. Qdrant (qdrant/qdrant)
    9. Chroma (chroma-core/chroma)
    10. GPT-4
    11. Claude 3
    12. Llama 3
    13. Tabnine Chat
    14. Codeium Chat

    AI recommended 14 alternatives but never named bgauryy/octocode-mcp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help transform complex codebases into AI-optimized knowledge for large language models?
    you: not recommended
    AI recommended (in order):
    1. Tree-sitter
    2. LlamaIndex
    3. LangChain
    4. Faiss
    5. Weaviate
    6. Pinecone
    7. Qdrant
    8. Hugging Face Transformers
    9. Jupyter Notebooks
    10. VS Code

    AI recommended 10 alternatives but never named bgauryy/octocode-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
    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 bgauryy/octocode-mcp?
    pass
    AI did not name bgauryy/octocode-mcp — likely talking about a different project

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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