官方说明10 个来源MCP 服务8 天前
jagan-shanmugam/mattermost-mcp-host
🐍 🏠 - Access Mattermost teams, channels and messages, plus an MCP host running as a Mattermost bot that can…
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官方说明🐍 🏠 - Access Mattermost teams, channels and messages, plus an MCP host running as a Mattermost bot that can use configured MCP servers.
配置模板（请按你的客户端调整）
claude mcp add mattermost-mcp-host -- npx -y mattermost-mcp-host
这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。
- ★ 数取值
- 34 ★ · jagan-shanmugam/mattermost-mcp-host · 取自该仓库的 GitHub 页面 · （2026-10-16 20:00:20 核对）
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 34 ★ · 10 个来源 · mcp
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
jagan-shanmugam/mattermost-mcp-host；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
Mattermost MCP Host
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
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
1. **Clone the repository:**
2. **Install:**
* Using uv (recommended):
3. **Configure Environment (.env file):**
Copy the .env.example and fill in the values or
Create a .env file in the project root (or set environment variables):
See .env.example for more options.
4. **Configure MCP Servers:**
Edit src/mattermost_mcp_host/mcp-servers.json to define the MCP servers you want to connect to. See src/mattermost_mcp_host/mcp-servers-example.json.
Depending on the server configuration, you might npx, uvx, docker installed in your system and in path.
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
1. **Clone the repository:**
2. **Install:**
* Using uv (recommended):
3. **Configure Environment (.env file):**
Copy the .env.example and fill in the values or
Create a .env file in the project root (or set environment variables):
See .env.example for more options.
4. **Configure MCP Servers:**
Edit src/mattermost_mcp_host/mcp-servers.json to define the MCP servers you want to connect to. See src/mattermost_mcp_host/mcp-servers-example.json.
Depending on the server configuration, you might npx, uvx, docker installed in your system and in path.
- 来源
- awesome · https://github.com/jagan-shanmugam/mattermost-mcp-host
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所属领域与标签
每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。