siva010928/multi-chat-mcp-server

🔥 UNIQUE FEATURE: Run multiple chat providers simultaneously with a single AI assistant!
Your AI can interact with Google Chat, Slack, Teams, and more—all at once.
Ask once: "Share this update with both Slack and Google Chat teams"
➡️ Learn more about multi-provider capabilities
Multi-Chat MCP Server is an open-source Python framework to build AI-powered chat integrations. Ships with full Google Chat support.
Keywords: Google Chat MCP • MCP Server Implementation • AI Chat Integration • Google Workspace Automation • Team Collaboration AI
---
🎯 What is Google Chat MCP Server?
**Google Chat MCP Server** is an open-source, production-ready **Model Control Protocol (MCP) server** designed for **Google Chat integration with AI assistants**. Built with an extensible **multi-provider architecture**, this project provides a robust foundation for integrating AI assistants with team chat platforms.
🏢 Built for Organizational Security & Privacy
**Important Security Note:** This tool is **designed for local, organizational use only**. We strongly recommend using this with **organization-provided on-premises LLM instances or your local installed LLM Agent Model** rather than cloud-based LLM model's to maintain complete control over your team's chat data and communications.
**Why Local/On-Premises Deployment:**
- **Data Privacy**: Keep sensitive team conversations within your organization
- **Security Compliance**: Meet enterprise security and compliance requirements
- **Full Control**: Maintain complete oversight of data flow and access
- **Custom Policies**: Implement organization-specific security measures
While anyone can adapt this tool for their particular use cases, it's designed with enterprise security as a priority.
Current Implementation Status
- ✅ **Google Chat Provider** - **Production Ready** with comprehensive API coverage
- 🔄 **Slack Provider** - Planned (contributions welcome)
- 📝 **Microsoft Teams Provider** - Planned (contributions welcome)
**Key Capability**: All providers can run simultaneously with a unified interface, allowing your AI assistant to seamlessly work across multiple chat platforms at once. Learn more about this powerful feature.
🧭 The Story Behind This Project
> *We even see open-source MCP servers for Google Chat — but not sure about Microsoft Teams or Slack, officially or in open source. However, even the ones that do exist fall short in real-world applicability. They offer limited functionalities that cannot handle full-context workflows like this project demonstrates.*
This **multi-provider MCP framework** was born from a real frustration experienced by development teams trying to leverage AI assistants in their daily workflows.
---
**The Original Problem**
Picture this scenario: You're debugging a complex issue, your AI assistant suggests a solution, but you need to check if your teammates have encountered something similar. You switch to Google Chat, scroll through hundreds of messages, copy-paste error logs, wait for responses, then manually relay the solution back to your AI assistant.
This constant context-switching was breaking the flow of productive AI-assisted development.
---
**The Breaking Point**
During a critical production incident, a developer spent 30 minutes manually shuttling information between Claude (via Cursor) and the team's Google Chat space. The AI had the technical knowledge to help. The team had the contextual experience.
But there was no bridge connecting these two knowledge sources.
That's when we realized:
> **AI assistants need to be participants in team collaboration — not isolated tools.**
**Our Solution:**
- **Seamless Integration**: AI assistants become active participants in team chat
- **Contextual Awareness**: AI can search team history for similar issues and solutions
- **Collaborative Problem-Solving**: AI can share problems with the team and implement their suggestions
- **Knowledge Bridging**: Connect AI technical knowledge with team experiential knowledge
🎯 Built for Developer Extensibility
🏗️ Modular Provider Architecture
Each chat platform is implemented as an independent module:
👥 Who's This For?
This project is designed for **two primary audiences**: