Multi-Chat MCP Server (Google Chat Included)
@siva010928
About Multi-Chat MCP Server (Google Chat Included)
Google Chat MCP server that lets AI assistants like Claude and Cursor participate directly in team conversations - search messages, help teammates, share files, and coordinate across chat platforms.
Config
Add this server to your MCP-compatible client using the configuration below.
{
"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"
]
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Multi-Chat MCP Server (Google Chat Included)?
Multi-Chat MCP Server is an open-source, production-ready MCP server for integrating AI assistants with Google Chat and other chat platforms. Built on a modular, multi-provider architecture, it is designed for developers and organizations who want their AI to participate directly in team collaboration while maintaining data privacy and security.
How to use Multi-Chat MCP Server (Google Chat Included)?
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.
Key features of Multi-Chat MCP Server (Google Chat Included)
- 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
Use cases of Multi-Chat MCP Server (Google Chat Included)
- AI assistant broadcasts error logs to Google Chat and applies team-suggested fixes
- AI pulls shared configuration files from chat and syncs them with the local environment
- Developer asks AI to summarize team activity or find mentions after being away
- AI proactively scans team conversations to assist with open concerns or blockers
- Multi-provider use: AI shares an update simultaneously to both Slack and Google Chat
FAQ from Multi-Chat MCP Server (Google Chat Included)
What makes this server different from other Google Chat MCP servers?
It is designed with a multi-provider architecture that allows running multiple chat providers (Google Chat, Slack, Teams) simultaneously via a single unified interface. The Google Chat provider is production-ready, while other providers are planned and open for contributions.
What are the dependencies and runtime requirements?
The server is built with Python and follows the MCP framework. It is designed for local or organization-provided on-premises LLM instances rather than cloud-based models, emphasizing data privacy and security compliance.
Where does my chat data live?
The tool is designed for local, organizational use only. Data remains within your organization's infrastructure when used with on-premises LLM instances, ensuring complete control over data flow and access.
Are Slack and Microsoft Teams supported yet?
Slack and Microsoft Teams providers are currently in the planning stage. The modular framework is ready for implementation, and contributions from the open-source community are welcome.
How do I switch between or configure multiple providers?
Each chat platform is implemented as an independent module within the provider architecture. To run multiple providers simultaneously, configure each provider's credentials and settings in the environment file.
Frequently asked questions
What makes this server different from other Google Chat MCP servers?
It is designed with a multi-provider architecture that allows running multiple chat providers (Google Chat, Slack, Teams) simultaneously via a single unified interface. The Google Chat provider is production-ready, while other providers are planned and open for contributions.
What are the dependencies and runtime requirements?
The server is built with Python and follows the MCP framework. It is designed for local or organization-provided on-premises LLM instances rather than cloud-based models, emphasizing data privacy and security compliance.
Where does my chat data live?
The tool is designed for local, organizational use only. Data remains within your organization's infrastructure when used with on-premises LLM instances, ensuring complete control over data flow and access.
Are Slack and Microsoft Teams supported yet?
Slack and Microsoft Teams providers are currently in the planning stage. The modular framework is ready for implementation, and contributions from the open-source community are welcome.
How do I switch between or configure multiple providers?
Each chat platform is implemented as an independent module within the provider architecture. To run multiple providers simultaneously, configure each provider's credentials and settings in the environment file.
Basic information
More AI & Agents MCP servers
mcp-hfspace MCP Server 🤗
evalstateMCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.
Intervals.icu MCP Server
mvilanovaModel Context Protocol (MCP) server for connecting Claude and ChatGPT with the Intervals.icu API.
欢迎来到 智言平台
Shy2593666979AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。
Transform MCP
UnstructuredThe Unstructured Transform MCP Server connects your ADK agent to Unstructured, a document processing platform that turns raw files into structured, AI-ready data. This integration gives your agent the ability to parse PD
Model Context Protocol for Unreal Engine
chongdashuEnable AI assistant clients like Cursor, Windsurf and Claude Desktop to control Unreal Engine through natural language using the Model Context Protocol (MCP).
Comments