Multi Chat MCP Server (Google Chat)
About
Connect AI assistants like Cursor to Google Chat and beyond — enabling smart, extensible collaboration across chat platforms.
Details
- Author
- siva010928
- GitHub stars
- 24
- Downloads
- 329
- Categories
- Communication, Community, Other, AI
Jump to
- Multi-provider architecture supports simultaneous chat platform connections
- Production-ready Google Chat integration with comprehensive API coverage
- Modular provider framework for extending to Slack, Teams, and custom platforms
- AI assistant can send, search, summarize, and reply in Google Chat
- Designed for local/on-premises deployment to keep data within the organization
- Supports real-time debugging, script sync, and team coordination workflows
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Multi Chat MCP Server (Google Chat)Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Configure the server by setting up the necessary environment variables and credentials for your chosen chat provider, then run the MCP server process. The Google Chat provider runs out of the box after proper configuration; future providers like Slack and Microsoft Teams will follow the same pluggable architecture. For multi-provider setups, define each provider's configuration in the designated environment file.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"multi chat mcp server (google chat)": {
"google_chat": {
"command": "uv",
"args": [
"--directory",
"/path/to/multi-chat-mcp-server",
"run",
"-m",
"src.server",
"--provider",
"google_chat"
]
},
"slack": {
"command": "uv",
"args": [
"--directory",
"/path/to/multi-chat-mcp-server",
"run",
"-m",
"src.server",
"--provider",
"slack"
]
}
}
}
}McpServers
{
"google_chat": {
"command": "uv",
"args": [
"--directory",
"/path/to/multi-chat-mcp-server",
"run",
"-m",
"src.server",
"--provider",
"google_chat"
]
},
"slack": {
"command": "uv",
"args": [
"--directory",
"/path/to/multi-chat-mcp-server",
"run",
"-m",
"src.server",
"--provider",
"slack"
]
}
}Multi-Chat MCP Server (Google Chat Included)
<div align="center">
<p style="background-color: #f8f9fa; padding: 15px; border-radius: 5px; border-left: 5px solid #4285f4; max-width: 800px; margin: 0 auto;">
<strong>🔥 UNIQUE FEATURE:</strong> Run <strong>multiple chat providers simultaneously</strong> with a single AI assistant!<br>
Your AI can interact with Google Chat, Slack, Teams, and more—all at once. <br>
Ask once: <em>"Share this update with both Slack and Google Chat teams"</em><br>
<a href="#running-multiple-chat-providers-simultaneously">➡️ Learn more about multi-provider capabilities</a>
</p>
</div>
<div align="center">
<h3>Multi-Chat MCP Server is an open-source Python framework to build AI-powered chat integrations. Ships with full Google Chat support.</h3>
<p>
<strong>Keywords:</strong> Google Chat MCP • MCP Server Implementation • AI Chat Integration • Google Workspace Automation • Team Collaboration AI
</p>
</div>
---
🎯 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:
src/providers/
├── google_chat/ # ✅ Complete implementation
├── slack/ # 📋 Framework ready for implementation
└── teams/ # 📋 Framework ready for implementation
👥 Who's This For?
This project is designed for two primary audiences:
🛠️ 1. Developers inside organizations
If you're a developer working in a team that uses Google Chat, and you're looking to integrate your AI IDEs (like Cursor, CodeWhisperer, or Copilot Chat) with team conversations — this MCP client will save you hours.
No more manually copying logs, checking for context, or waiting for someone to see your question.
Your AI agent can now directly:
Search your chat history for relevant past discussions
Share code snippets or error logs automatically
Receive responses and convert them into actionable fixes
Summarize ongoing team activities
Fetch missing config/scripts from shared spaces
💡 2. Open source contributors & AI platform builders
If you're building AI-powered tools, IDE integrations, or internal assistants — this is your starting point for a multi-provider MCP architecture.
You can fork this project to:
Extend support for Slack, Microsoft Teams, or custom messaging platforms
Build your own custom AI workflows on top of MCP
🧩 Google Chat MCP Server – Real-world Usage Showcase
These walkthroughs show how an AI assistant, powered by this MCP server, evolves from a passive tool into an active collaborator — debugging issues, coordinating teams, syncing scripts, and proactively unblocking developers.
---
🛠️ Tool Setup & Initialization
<div align="center">
<p><i><strong>Scene 1: Tool Registration with Google Chat</strong></i></p>
</div>
The Scenario: Connecting MCP client to Google Chat.
What's Happening: The AI assistant is granted access to all Google Chat tools (e.g., send, search, summarize, attach, reply).
Why it Matters: The assistant can now act* inside Google Chat, not just observe.
---
🧯 Debugging & Resolution (Docker Example)
<div align="center">
<p><i><strong>Scene 2: Broadcasting an Error to the Team</strong></i></p>
</div>
What's Happening: A developer asks the AI to share Docker error logs in chat, prompting real-time team help.
<div align="center">
<p><i><strong>Scene 3: Team Responds with a Fix</strong></i></p>
</div>
Next Step: A teammate replies with a Dockerfile fix (COPY requirements.txt .).
…
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