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#chat

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MCP-MESSENGER

**SlashMCP** is a production-grade AI workspace that connects LLMs to real-world data and tools through an intuitive chat interface. Built on the Model Context Protocol (MCP), it enables seamless interaction with multiple AI providers (OpenAI, Claude, Gemini) while providing powerful capabilities for document analysis, financial data queries, web scraping, and multi-agent workflow orchestration. ### Key Features: - **Multi-LLM Support**: Switch between GPT-4, Claude, and Gemini at runtime—no restart needed - **Smart Command Autocomplete**: Type `/` to discover and execute MCP server commands instantly - **Document Intelligence**: Drag-and-drop documents with automatic OCR extraction and vision analysis - **Financial Data Integration**: Real-time stock quotes, charts, and prediction market data via Alpha Vantage and Polymarket - **Browser Automation**: Web scraping and navigation using Playwright MCP - **Multi-Agent Orchestration**: Intelligent routing with specialized agents for command discovery, tool execution, and response synthesis - **Dynamic MCP Registry**: Add and use any MCP server on the fly without code changes - **Voice Interaction**: Browser-based transcription and text-to-speech support ### Use Cases: - Research and analysis workflows - Document processing and extraction - Financial market monitoring - Web data collection and comparison - Multi-step task automation **Live Demo:** [ slashmcp.vercel.app ]( https://slashmcp.vercel.app ) **GitHub:** [ github.com/mcpmessenger/slashmcp ]( https://github.com/mcpmessenger/slashmcp ) **Website:** [ slashmcp.com](https://slashmcp.com )

Google Chat MCP Server: Extensible to Teams & More, Supports Simultaneous Chat Connections

Multi-Chat MCP Server is an open-source Python framework that enables integration of AI assistants into team chat environments, with support for multiple chat platforms like Google Chat, Slack, and Microsoft Teams. The core purpose is to bridge the gap between isolated AI tools and collaborative team workflows by allowing the AI to participate in conversations, share updates, fetch historical context, and respond to developer prompts directly within team messaging spaces. The project is built around the Model Control Protocol (MCP) concept, which facilitates structured communication between AI clients and external systems. In this case, it implements MCP to let AI assistants connect with messaging platforms. Google Chat integration is complete and production-ready. Other providers like Slack and Teams are scaffolded but not fully implemented yet. The server runs locally and is intended for organizational use, especially in environments where data privacy, security compliance, and full control over communication flows are important. This is particularly useful for organizations running on-premises LLMs or secure internal deployments, as the server does not rely on any third-party LLMs or cloud-hosted models by default. The architecture is modular, so additional chat platforms can be added by following the structure used for Google Chat. The project does not offer any end-user UI or GUI. It is a backend integration tool meant to be used with AI IDE clients (like Cursor or Claude) that understand how to interact with an MCP server.