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WhatsApp MCP Server

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WhatsApp MCP Server

Hi HN – I built an open-source, self-hosted Model Context Protocol (MCP) server for WhatsApp: https://github.com/lharries/whatsapp-mcp It connects to your personal WhatsApp account via the WhatsApp Web multi-device API (using whatsmeow from the Beeper team), and doesn't rely on third-party APIs. All messages are stored locally in SQLite. Nothing is sent to the cloud unless you explicitly allow your LLM to access the data via tools – so you maintain full control and privacy. The MCP server can: - Search your messages, contacts, and groups - Send WhatsApp messages to individuals or groups Why build this? 99% of your life is stored in WhatsApp, by connecting an LLM to WhatsApp you get all this context. And your AI agent can execute tasks on your behalf by sending messages.

Visit productView on Hacker NewsMar 31, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, mcp · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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