@siva010928
Basic information
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.
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"
]
}
}
}Tool list not available yet
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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.
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.
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.
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.
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.
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.
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.
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