RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/mcpdoc

Default branch main · commit 31bbc25a · scanned 6/28/2026, 12:21:58 AM

GitHub: 1,009 stars · 124 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 langchain-ai/mcpdoc, 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's opening to clarify purpose and audience

    Why:

    CURRENT
    # MCP LLMS-TXT Documentation Server

## Overview

llms.txt is a website index for LLMs, providing background information, guidance, and links to detailed markdown files. IDEs like Cursor and Windsurf or apps like Claude Code/Desktop can use `llms.txt` to retrieve context for tasks. However, these apps use different built-in tools to read and process files like `llms.txt`. The retrieval process can be opaque, and there is not always a way to audit the tool calls or the context returned.

MCP offers a way for developers to have *full control* over tools used by these applications. Here, we create an open source MCP server to provide MCP host applications (e.g., Cursor, Windsurf, Claude Code/Desktop) with (1) a user-defined list of `llms.txt` files and (2) a simple `fetch_docs` tool read URLs within any of the provided `llms.txt` files. This allows the user to audit each tool call as well as the context returned.
    COPY-PASTE FIX
    # MCP LLMS-TXT Documentation Server: Custom, Auditable Context for LLM-Integrated IDEs

This open-source server enables developers to serve user-defined `llms.txt` documentation files directly to LLM-integrated development environments (IDEs) and applications like Cursor, Windsurf, and Claude Code/Desktop. It provides full control and auditability over the documentation context retrieved by AI assistants, addressing the opacity of built-in retrieval tools.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    agents, claude-code, cursor, ide, llms, llms-txt
    COPY-PASTE FIX
    agents, claude-code, cursor, ide, llms, llms-txt, documentation-server, custom-docs, llm-context, auditability, developer-tools
  • mediumabout#3
    Refine the repository description for clarity

    Why:

    CURRENT
    Expose llms-txt to IDEs for development
    COPY-PASTE FIX
    An open-source server to provide custom, auditable llms.txt documentation context to LLM-integrated IDEs like Cursor and Claude Code.

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 langchain-ai/mcpdoc
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 1×
  2. Codeium · recommended 1×
  3. Tabnine · recommended 1×
  4. davidhalter/jedi · recommended 1×
  5. Dash · recommended 1×
  • CATEGORY QUERY
    How can I audit documentation retrieval by AI coding assistants in my IDE?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Codeium
    3. Tabnine
    4. Jedi (davidhalter/jedi)
    5. Dash
    6. Zeal (zealdocs/zeal)
    7. Browser Developer Tools
    8. VS Code (microsoft/vscode)
    9. PyCharm
    10. IntelliJ
    11. Sublime Text
    12. Notepad++ (notepad-plus-plus/notepad-plus-plus)
    13. Obsidian
    14. Notion

    AI recommended 14 alternatives but never named langchain-ai/mcpdoc. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to serve custom technical documentation to LLM-integrated development environments?
    you: not recommended
    AI recommended (in order):
    1. Read the Docs
    2. Docusaurus
    3. MkDocs
    4. GitBook
    5. Sphinx
    6. Next.js
    7. Nuxt.js

    AI recommended 7 alternatives but never named langchain-ai/mcpdoc. 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 langchain-ai/mcpdoc?
    pass
    AI did not name langchain-ai/mcpdoc — 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 langchain-ai/mcpdoc in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/mcpdoc 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 langchain-ai/mcpdoc solve, and who is the primary audience?
    pass
    AI named langchain-ai/mcpdoc explicitly

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

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langchain-ai/mcpdoc — 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