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A Mattermost integration using LangGraph to connect to multiple MCP servers, enabling AI agent interaction and tool execution within Mattermost channels.
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A Mattermost integration using LangGraph to connect to multiple MCP servers, enabling AI agent interaction and tool execution within Mattermost channels.
This integration offers a convenient way to interact with MCP servers through Mattermost. However, the ability of the AI agent to execute tools and the reliance on stdio connections introduce moderate risks. It is safe when MCP servers are properly configured and the agent's actions are carefully monitored. It is risky if MCP servers have vulnerabilities or the agent is given excessive autonomy.
Performance depends on the LLM provider, network latency, and the complexity of the MCP tools being executed. Consider optimizing LLM prompts and MCP server configurations for better performance.
Cost is primarily driven by LLM API usage (tokens) and the resources consumed by the MCP servers. Monitor LLM usage and optimize tool execution to minimize costs.
echoEchos back the message provided as an argument.
Read-only operation, no side effects.
github_create_issueCreates a new issue in a specified GitHub repository.
Creates a new resource in an external system.
web_searchSearches the internet for information based on a query.
Read-only access to public information.
Token
hybrid
post_allcreate_postread_channelcreate_direct_channelread_userThis integration offers a convenient way to interact with MCP servers through Mattermost. However, the ability of the AI agent to execute tools and the reliance on stdio connections introduce moderate risks. It is safe when MCP servers are properly configured and the agent's actions are carefully monitored. It is risky if MCP servers have vulnerabilities or the agent is given excessive autonomy.
The agent's autonomy is determined by the LLM and the available tools. There is no built-in sandboxing or rollback support.
Production Tip
Implement robust monitoring and logging to track agent activity and identify potential issues.
Azure OpenAI is the default, but OpenAI, Anthropic Claude, and Google Gemini are also supported via environment variables.
Edit the `mcp-servers.json` file and define the connection details for the new server.
Use the command prefix followed by the server name, 'call', the tool name, and a JSON string of arguments. Example: `#my-server call echo '{"message": "Hello MCP!"}'`
The bot account needs `post_all`, `create_post`, `read_channel`, `create_direct_channel`, and `read_user` permissions.
The integration passes the thread history to the LangGraph agent, allowing it to maintain conversational context.
The integration is designed to work with cloud-based LLM providers. Integrating with self-hosted LLMs would require modifications to the code.
You can control which tools are available to the agent by configuring the MCP servers and their exposed tools.