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Fork of lharries/whatsapp-mcp that fixes Unicode/Hebrew search and adds dual-bridge support for personal and business WhatsApp accounts. The upstream project provides 13 MCP tools for reading and sending WhatsApp messages through a Go bridge + Python MCP server. Our fork replaces SQLite LOWER()+LIKE queries (which silently break for non-ASCII text) with instr()-based matching that works for Hebrew, Arabic, emoji, and all Unicode. Also adds auto-detection of business bridge databases and an optional self-chat safety restriction for LLM interactions.
Replaced SQLite LOWER()+LIKE (ASCII-only) with instr()-based matching. Works for Hebrew, Arabic, emoji, CJK — any Unicode text.
Auto-detects business bridge database. Run personal (port 8741) and business (port 8742) WhatsApp accounts simultaneously without manual config.
Optional WHATSAPP_OWNER_JID restricts sends to your own Saved Messages. Safe for LLM interactions — Claude can read all chats but only send to you.
Personal + Business
Go Bridge
whatsmeow + REST API
SQLite
Message store
Python MCP
13 tools, instr() search
AI Agent
Claude / Cursor
Personal + Business
Go Bridge
whatsmeow + REST API
SQLite
Message store
Python MCP
13 tools, instr() search
AI Agent
Claude / Cursor
git clone https://github.com/EtanHey/whatsapp-mcp.git
cd whatsapp-mcp && cd whatsapp-bridge && go buildEnterprise-grade AI-powered recruitment platform with voice interviews, resume screening, multi-channel communication, and seamless ATS/HRIS integrations. Automates hiring workflows for Quick Service Restaurants, healthcare, and construction industries.
8 MCP tools, 335K+ indexed chunks, hybrid semantic+keyword search, knowledge graph with entity resolution, local LLM enrichment via Groq/MLX/Ollama. pip install brainlayer.
A production-ready computer vision model that detects and classifies hands, arms, and non-hand objects in real-time with 96% accuracy.
9 read tools (search contacts, list messages, get chat, download media) + 4 write tools (send message, send file, send audio with auto Opus conversion).
2 MCP tools (voice_speak + voice_ask), 5 voice modes, whisper.cpp STT (~300ms), edge-tts, macOS Voice Bar widget, session booking. 236 tests. bunx voicelayer-mcp.