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
- License
- MIT
Explore
- 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
- Python 3.9+
- UV Package Manager (recommended)
- Google Cloud Project with Google Chat API enabled
- MCP Client (Claude Desktop, Cursor, or other MCP-compatible AI assistant)
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.
---
<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.
---
uv venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
uv pip install -r requirements.txt
python -m src.server --provider google_chat -local-auth
For comprehensive setup instructions including Google Cloud configuration, OAuth setup, and troubleshooting, see our Complete Google Chat MCP Setup Guide - This detailed implementation guide covers:
- Google Cloud Project setup and API enablement
- OAuth 2.0 configuration and security best practices
- Step-by-step authentication flow
- Common setup issues and their solutions
One of the key advantages of Multi Chat MCP Server is the ability to run multiple chat providers simultaneously. Each provider runs in its own server instance, allowing your AI assistant to interact with multiple platforms at once.
For example, you can configure both Google Chat and Slack MCP servers to run simultaneously:
{
"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"
]
}
}
}
With this setup, your AI assistant can:
- Access tools from all configured providers simultaneously
- Execute cross-platform actions with a single command
- Perform platform-specific operations through named providers
providers:
google_chat:
name: "Google Chat MCP Server"
description: "Production-ready Google Chat MCP integration"
token_path: "src/providers/google_chat/token.json"
credentials_path: "src/providers/google_chat/credentials.json"
callback_url: "http://localhost:8000/auth/callback"
<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.
---
Interact with Google Chat using the tools below. Each tool includes its source file and parameters.
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"
]
}
}<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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