Mattermost 集成，连接模型上下文协议（MCP）服务器，利用基于 LangGraph 的 AI 代理，提供智能界面，直接在 Mattermost 内与用户交互和执行工具。

演示

1.支持频道中的Github代理——搜索现有问题和PR，如果找不到会创建新问题

2.在网上搜索并使用 Mattermost-MCP-server 发布到频道

在下方滚动查看YouTube上的完整演示

特色

• 🤖 Langgraph代理集成：使用LangGraph代理理解用户请求并协调响应。
• 🔌 MCP 服务器集成：连接多个定义在 mcp-servers.json 中的 MCP 服务器。
• 🛠️ 动态工具加载：自动从连接的MCP服务器中发现工具并向AI代理开放。将MCP工具转换为langchain结构化工具。
• 💬 线程感知对话：在Mattermost线程内保持对话上下文，确保互动连贯。
• 🔄 智能工具使用：AI代理可以决定何时使用可用工具（包括串联多重呼叫）以满足用户请求。

Mattermost MCP Host

A Mattermost integration that connects to Model Context Protocol (MCP) servers, leveraging a LangGraph-based AI agent to provide an intelligent interface for interacting with users and executing tools directly within Mattermost.

Demo

1. Github Agent in support channel - searches the existing issues and PRs and creates a new issue if not found

2. Search internet and post to a channel using Mattermost-MCP-server

Scroll below for full demo in YouTube

Features

• 🤖 Langgraph Agent Integration: Uses a LangGraph agent to understand user requests and orchestrate responses.
• 🔌 MCP Server Integration: Connects to multiple MCP servers defined in mcp-servers.json.
• 🛠️ Dynamic Tool Loading: Automatically discovers tools from connected MCP servers and makes them available to the AI agent. Converts MCP tools to langchain structured tools.
• 💬 Thread-Aware Conversations: Maintains conversational context within Mattermost threads for coherent interactions.
• 🔄 Intelligent Tool Use: The AI agent can decide when to use available tools (including chaining multiple calls) to fulfill user requests.
• 🔍 MCP Capability Discovery: Allows users to list available servers, tools, resources, and prompts via direct commands.
• #️⃣ Direct Command Interface: Interact directly with MCP servers using a command prefix (default: #).

Overview

The integration works as follows:

1. Mattermost Connection (mattermost_client.py): Connects to the Mattermost server via API and WebSocket to listen for messages in a specified channel.
2. MCP Connections (mcp_client.py): Establishes connections (primarily stdio) to each MCP server defined in src/mattermost_mcp_host/mcp-servers.json. It discovers available tools on each server.
3. Agent Initialization (agent/llm_agent.py): A LangGraphAgent is created, configured with the chosen LLM provider and the dynamically loaded tools from all connected MCP servers.
4. Message Handling (main.py):
   • If a message starts with the command prefix (#), it’s parsed as a direct command to list servers/tools or call a specific tool via the corresponding MCPClient.
   • Otherwise, the message (along with thread history) is passed to the LangGraphAgent.
5. Agent Execution: The agent processes the request, potentially calling one or more MCP tools via the MCPClient instances, and generates a response.
6. Response Delivery: The final response from the agent or command execution is posted back to the appropriate Mattermost channel/thread.

Setup