RRepoGEO

REPOGEO REPORT · LITE

forloopcodes/contextplus

Default branch main · commit 393875a2 · scanned 5/15/2026, 6:33:04 AM

GitHub: 1,895 stars · 152 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 forloopcodes/contextplus, 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 the README's opening paragraph to clarify its domain

    Why:

    CURRENT
    Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.
    COPY-PASTE FIX
    Context+ is a **Semantic Intelligence server for large-scale engineering codebases**, not a context management library for async operations. Designed for developers who demand 99% accuracy, Context+ combines RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking to turn a massive codebase into a searchable, hierarchical feature graph.
  • hightopics#2
    Expand repository topics to reflect core functionality

    Why:

    CURRENT
    mcp-server
    COPY-PASTE FIX
    semantic-search, code-analysis, rag, tree-sitter, ast, codebase-intelligence, developer-tools, ai-for-code, knowledge-graph
  • mediumreadme#3
    Add a 'What it's NOT' section to the README

    Why:

    COPY-PASTE FIX
    ## What Context+ Is NOT

Context+ is **not** a library for managing execution context in asynchronous Python applications (e.g., like `contextvars` or `threading.local()`). It is a standalone server focused on deep semantic understanding and navigation of large codebases. Its purpose is to transform code into an intelligent, queryable knowledge graph, not to manage runtime state.

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 forloopcodes/contextplus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Code Search
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Code Search · recommended 2×
  2. Sourcegraph · recommended 1×
  3. OpenGrok · recommended 1×
  4. LSIF · recommended 1×
  5. VS Code · recommended 1×
  • CATEGORY QUERY
    How to efficiently navigate and understand a massive codebase with semantic search capabilities?
    you: not recommended
    AI recommended (in order):
    1. Sourcegraph
    2. OpenGrok
    3. LSIF
    4. VS Code
    5. Kite
    6. GitHub Code Search
    7. Elasticsearch
    8. tree-sitter

    AI recommended 8 alternatives but never named forloopcodes/contextplus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide AST-based code analysis and RAG for intelligent codebase understanding?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. Tree-sitter (tree-sitter/tree-sitter)
    3. ANTLR (antlr/antlr4)
    4. CodeQL (github/codeql)
    5. OpenGrok (oracle/opengrok)
    6. Sourcegraph (sourcegraph/sourcegraph)
    7. GitHub Code Search

    AI recommended 7 alternatives but never named forloopcodes/contextplus. 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 forloopcodes/contextplus?
    pass
    AI named forloopcodes/contextplus explicitly

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

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

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

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