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

1. Clone the repository:

   git clone 
   cd mattermost-mcp-host
   

2. Install:

   • Using uv (recommended):

     # Install uv if you don't have it yet
     # curl -LsSf https://astral.sh/uv/install.sh | sh 
     
     # Activate venv
     source .venv/bin/activate
     
     # Install the package with uv
     uv sync
     
     # To install dev dependencies
     uv sync --dev --all-extras
     

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):

   # Mattermost Details
   MATTERMOST_URL=http://your-mattermost-url
   MATTERMOST_TOKEN=your-bot-token # Needs permissions to post, read channel, etc.
   MATTERMOST_TEAM_NAME=your-team-name
   MATTERMOST_CHANNEL_NAME=your-channel-name # Channel for the bot to listen in
   # MATTERMOST_CHANNEL_ID= # Optional: Auto-detected if name is provided
   
   # LLM Configuration (Azure OpenAI is default)
   DEFAULT_PROVIDER=azure
   AZURE_OPENAI_ENDPOINT=your-azure-endpoint
   AZURE_OPENAI_API_KEY=your-azure-api-key
   AZURE_OPENAI_DEPLOYMENT=your-deployment-name # e.g., gpt-4o
   # AZURE_OPENAI_API_VERSION= # Optional, defaults provided
   
   # Optional: Other providers (install with `[all]` extra)
   # OPENAI_API_KEY=...
   # ANTHROPIC_API_KEY=...
   # GOOGLE_API_KEY=...
   
   # Command Prefix
   COMMAND_PREFIX=# 
   

   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.

5. Start the Integration:

   mattermost-mcp-host
   

Prerequisites

• Python 3.13.1+
• uv package manager
• Mattermost server instance
• Mattermost Bot Account with API token
• Access to a LLM API (Azure OpenAI)

Optional

• One or more MCP servers configured in mcp-servers.json
• Tavily web search requires TAVILY_API_KEY in .env file

Usage in Mattermost

Once the integration is running and connected:

1. Direct Chat: Simply chat in the configured channel or with the bot. The AI agent will respond, using tools as needed. It maintains context within message threads.
2. Direct Commands: Use the command prefix (default #) for specific actions:
   • #help - Display help information.
   • #servers - List configured and connected MCP servers.
   • # tools - List available tools for ``.
   • # call - Call on with arguments provided as a JSON string.
     • Example: #my-server call echo '{"message": "Hello MCP!"}'
   • # resources - List available resources for ``.
   • # prompts - List available prompts for ``.

Next Steps

• ⚙️ Configurable LLM Backend: Supports multiple AI providers (Azure OpenAI default, OpenAI, Anthropic Claude, Google Gemini) via environment variables.

Mattermost Setup

1. Create a Bot Account

• Go to Integrations > Bot Accounts > Add Bot Account
• Give it a name and description
• Save the access token in the .env file

2. Required Bot Permissions

• post_all
• create_post
• read_channel
• create_direct_channel
• read_user

3. Add Bot to Team/Channel

• Invite the bot to your team
• Add bot to desired channels

Troubleshooting

1. Connection Issues

• Verify Mattermost server is running
• Check bot token permissions
• Ensure correct team/channel names

2. AI Provider Issues

• Validate API keys
• Check API quotas and limits
• Verify network access to API endpoints

3. MCP Server Issues

• Check server logs
• Verify server configurations
• Ensure required dependencies are installed and env variables are defined

Demos

Create issue via chat using Github MCP server

(in YouTube)

Contributing

Please feel free to open a PR.

License

This project is licensed under the MIT License - see the LICENSE file for details.