RRepoGEO

REPOGEO REPORT · LITE

lharries/whatsapp-mcp

Default branch main · commit 7d6a06dc · scanned 5/10/2026, 7:06:36 AM

GitHub: 5,594 stars · 1,022 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 lharries/whatsapp-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 the README's opening sentence to emphasize LLM integration with personal WhatsApp data.

    Why:

    CURRENT
    This is a Model Context Protocol (MCP) server for WhatsApp. With this you can search and read your personal Whatsapp messages (including images, videos, documents, and audio messages), search your contacts and send messages to either individuals or groups.
    COPY-PASTE FIX
    This is a Model Context Protocol (MCP) server designed to connect your **personal WhatsApp account** directly to an **AI agent or LLM**. It enables your AI to search, read, and send messages (including media) from your personal chats, with all data stored locally and accessed via tools you control.
  • mediumtopics#2
    Add more specific topics to highlight LLM agent integration and personal data management.

    Why:

    CURRENT
    ai, mcp, whatsapp, whatsapp-api
    COPY-PASTE FIX
    ai, mcp, whatsapp, llm-agent, personal-data, local-first, privacy
  • lowabout#3
    Expand the repository description to clarify its purpose for personal AI agent integration.

    Why:

    CURRENT
    WhatsApp MCP server
    COPY-PASTE FIX
    A Model Context Protocol (MCP) server to connect your personal WhatsApp account with AI agents and LLMs, enabling local, privacy-focused message processing.

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 lharries/whatsapp-mcp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
WhatsApp Chat Export
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. WhatsApp Chat Export · recommended 1×
  2. Telegram Export · recommended 1×
  3. Facebook Messenger Download Your Information · recommended 1×
  4. Google Takeout · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How can I integrate an AI agent with my personal chat messages for processing?
    you: not recommended
    AI recommended (in order):
    1. WhatsApp Chat Export
    2. Telegram Export
    3. Facebook Messenger Download Your Information
    4. Google Takeout
    5. LangChain
    6. LlamaIndex
    7. Haystack
    8. Telegram Bot API
    9. python-telegram-bot
    10. Discord Bots
    11. discord.py
    12. WhatsApp Business API
    13. Selenium
    14. PyAutoGUI
    15. OpenAI's GPT-4

    AI recommended 15 alternatives but never named lharries/whatsapp-mcp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a local server to manage and programmatically access personal messaging data.
    you: not recommended
    AI recommended (in order):
    1. Mailcow: Dockerized
    2. Synapse
    3. Nextcloud Talk
    4. Prosody
    5. Rocket.Chat
    6. Mattermost

    AI recommended 6 alternatives but never named lharries/whatsapp-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 lharries/whatsapp-mcp?
    pass
    AI named lharries/whatsapp-mcp explicitly

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

  • If a team adopts lharries/whatsapp-mcp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lharries/whatsapp-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 lharries/whatsapp-mcp solve, and who is the primary audience?
    pass
    AI did not name lharries/whatsapp-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?

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  • Brand-free category queries5 vs 2 in Lite
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