Official description10 sourcesMCP servers8d ago
jagan-shanmugam/mattermost-mcp-host
๐ ๐ - Access Mattermost teams, channels and messages, plus an MCP host running as a Mattermost bot that canโฆ
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Official description๐ ๐ - Access Mattermost teams, channels and messages, plus an MCP host running as a Mattermost bot that can use configured MCP servers.
Config template (adjust for your client)
claude mcp add mattermost-mcp-host -- npx -y mattermost-mcp-host
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
- Star count
- 34 โ ยท jagan-shanmugam/mattermost-mcp-host ยท read from the repository page ยท (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list ยท type: MCP server
- Harvest history
- first seen 2026-10-09 ยท last seen 2026-10-16 ยท seen 32ร ยท listed by 10 sources
- Score
- score 1,003 ยท 34 โ ยท 10 sources ยท mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP ๆๅก. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
jagan-shanmugam/mattermost-mcp-host๏ผfirst catalogued 2026-10-09๏ผlast seen 2026-10-16ใThere is no official certification or quality rating here โ we record sources and dates only.Third-party quality signals
Mattermost MCP Host
ไปฅไธๆๅฝ่ช่ฏฅไปๅบ็ README๏ผ็ๆฎต๏ผ ยท ๅจ GitHub ่ฏปๅฎๆด README โ
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
- ๐ค **Langgraph Agent Integration**: Uses a LangGraph agent to understand user requests and orchestrate responses.
- ๐ **MCP Server Integration**: Connects to multiple MCP servers defined in mcp-servers.json.
- ๐ ๏ธ **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.
- ๐ฌ **Thread-Aware Conversations**: Maintains conversational context within Mattermost threads for coherent interactions.
- ๐ **Intelligent Tool Use**: The AI agent can decide when to use available tools (including chaining multiple calls) to fulfill user requests.
- ๐ **MCP Capability Discovery**: Allows users to list available servers, tools, resources, and prompts via direct commands.
- #๏ธโฃ **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
- ๐ค **Langgraph Agent Integration**: Uses a LangGraph agent to understand user requests and orchestrate responses.
- ๐ **MCP Server Integration**: Connects to multiple MCP servers defined in mcp-servers.json.
- ๐ ๏ธ **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.
- ๐ฌ **Thread-Aware Conversations**: Maintains conversational context within Mattermost threads for coherent interactions.
- ๐ **Intelligent Tool Use**: The AI agent can decide when to use available tools (including chaining multiple calls) to fulfill user requests.
- ๐ **MCP Capability Discovery**: Allows users to list available servers, tools, resources, and prompts via direct commands.
- #๏ธโฃ **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.
- Source
- awesome ยท https://github.com/jagan-shanmugam/mattermost-mcp-host
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