RRepoGEO

REPOGEO REPORT · LITE

ktnyt/cclsp

Default branch main · commit 93414a12 · scanned 6/14/2026, 4:42:08 PM

GitHub: 652 stars · 49 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 ktnyt/cclsp, 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 H1 and opening paragraph to emphasize LLM-LSP integration

    Why:

    CURRENT
    # cclsp - not your average LSP adapter

**cclsp** is a Model Context Protocol (MCP) server that seamlessly integrates LLM-based coding agents with Language Server Protocol (LSP) servers. LLM-based coding agents often struggle with providing accurate line/column numbers, which makes naive attempts to integrate with LSP servers fragile and frustrating. cclsp solves this by intelligently trying multiple position combinations and providing robust symbol resolution that just works, no matter how your AI assistant counts lines.
    COPY-PASTE FIX
    # cclsp: The Claude Code LSP Adapter for Robust LLM-LSP Integration

**cclsp** is a Model Context Protocol (MCP) server designed to seamlessly integrate LLM-based coding agents, such as Claude Code, with any Language Server Protocol (LSP) server. It specifically solves the common problem of LLM-based agents struggling with accurate line/column numbers, providing robust symbol resolution that just works, regardless of how your AI assistant counts lines.
  • mediumtopics#2
    Add more specific AI/LLM integration topics

    Why:

    CURRENT
    claude, claude-code, lsp, mcp, mcp-server
    COPY-PASTE FIX
    claude, claude-code, lsp, mcp, mcp-server, ai-coding-assistant, llm-integration, code-agent-integration
  • mediumreadme#3
    Add a 'Comparison' or 'What it's not' section to clarify unique positioning

    Why:

    COPY-PASTE FIX
    ## What cclsp is (and isn't)
cclsp is not a language-specific LSP server (like `rust-analyzer` or `pyright`), nor is it an AI coding agent itself (like `GitHub Copilot` or `Code Llama`). Instead, cclsp acts as a crucial adapter, enabling *any* LLM-based coding agent to reliably interact with *any* existing LSP server, specifically by resolving the common issue of inaccurate line/column number reporting from AI models.

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 ktnyt/cclsp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rust-lang/rust-analyzer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. rust-lang/rust-analyzer · recommended 2×
  2. microsoft/pyright · recommended 2×
  3. Code Llama · recommended 2×
  4. github/copilot.vim · recommended 1×
  5. github/copilot.nvim · recommended 1×
  • CATEGORY QUERY
    How to integrate AI coding assistants with existing Language Server Protocol implementations?
    you: not recommended
    AI recommended (in order):
    1. Copilot.vim (github/copilot.vim)
    2. Copilot.nvim (github/copilot.nvim)
    3. coc.nvim (neoclide/coc.nvim)
    4. nvim-lspconfig (neovim/nvim-lspconfig)
    5. GitHub Copilot
    6. Codeium
    7. Tabnine (tabnine/Tabnine-VSCode)
    8. IntelliJ IDEA
    9. AI Assistant
    10. PyCharm
    11. rust-analyzer (rust-lang/rust-analyzer)
    12. pyright (microsoft/pyright)
    13. Tabby (TabbyML/tabby)
    14. Code Llama

    AI recommended 14 alternatives but never named ktnyt/cclsp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a solution for LLM code agents providing inaccurate line numbers to LSP.
    you: not recommended
    AI recommended (in order):
    1. Neovim (neovim/neovim)
    2. nvim-treesitter (nvim-treesitter/nvim-treesitter)
    3. VS Code (microsoft/vscode)
    4. Tree-sitter (tree-sitter/tree-sitter)
    5. Pyright (microsoft/pyright)
    6. rust-analyzer (rust-lang/rust-analyzer)
    7. typescript-language-server (typescript-language-server/typescript-language-server)
    8. ast
    9. syn (dtolnay/syn)
    10. Babel (babel/babel)
    11. TypeScript's own compiler API
    12. Sourcegraph (sourcegraph/sourcegraph)
    13. ctags (universal-ctags/ctags)
    14. cscope (cscope-devs/cscope)
    15. Llama 2
    16. Code Llama
    17. GPT-3.5
    18. GPT-4

    AI recommended 18 alternatives but never named ktnyt/cclsp. 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 ktnyt/cclsp?
    pass
    AI named ktnyt/cclsp explicitly

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

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

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

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ktnyt/cclsp — 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