# mcp-client-slackbot

**Repository Path**: mirrors_msabramo/mcp-client-slackbot

## Basic Information

- **Project Name**: mcp-client-slackbot
- **Description**: Simple Slackbot MCP Client
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-21
- **Last Updated**: 2026-05-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MCP Simple Slackbot

A simple Slack bot that uses the Model Context Protocol (MCP) to enhance its capabilities with external tools.

## Features

![2025-03-08-ezgif com-video-to-gif-converter](https://github.com/user-attachments/assets/0e2b6e1c-80f2-48c3-8ca4-1c41f3678478)

- **AI-Powered Assistant**: Responds to messages in channels and DMs using LLM capabilities
- **MCP Integration**: Full access to MCP tools like SQLite database and web fetching
- **Multi-LLM Support**: Works with OpenAI, Groq, and Anthropic models
- **App Home Tab**: Shows available tools and usage information

## Setup

### 1. Create a Slack App

1. Go to [api.slack.com/apps](https://api.slack.com/apps) and click "Create New App"
2. Choose "From an app manifest" and select your workspace
3. Copy the contents of `mcp_simple_slackbot/manifest.yaml` into the manifest editor
4. Create the app and install it to your workspace
5. Under the "Basic Information" section, scroll down to "App-Level Tokens"
6. Click "Generate Token and Scopes" and:
   - Enter a name like "mcp-assistant"
   - Add the `connections:write` scope
   - Click "Generate"
7. Take note of both your:
   - Bot Token (`xoxb-...`) found in "OAuth & Permissions"
   - App Token (`xapp-...`) that you just generated

### 2. Install Dependencies

```bash
# Create a virtual environment
python -m venv venv

# Activate the virtual environment
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install project dependencies
pip install -r mcp_simple_slackbot/requirements.txt
```

### 3. Configure Environment Variables

Create a `.env` file in the `mcp_simple_slackbot` directory (see `.env.example` for a template):

```
# Slack API credentials
SLACK_BOT_TOKEN=xoxb-your-token
SLACK_APP_TOKEN=xapp-your-token

# LLM API credentials
OPENAI_API_KEY=sk-your-openai-key
# or use GROQ_API_KEY or ANTHROPIC_API_KEY

# LLM configuration
LLM_MODEL=gpt-4-turbo
```

## Running the Bot

```bash
# Navigate to the module directory
cd mcp_simple_slackbot

# Run the bot directly
python main.py
```

The bot will:
1. Connect to all configured MCP servers
2. Discover available tools
3. Start the Slack app in Socket Mode
4. Listen for mentions and direct messages

## Usage

- **Direct Messages**: Send a direct message to the bot
- **Channel Mentions**: Mention the bot in a channel with `@MCP Assistant`
- **App Home**: Visit the bot's App Home tab to see available tools

## Architecture

The bot is designed with a focused architecture:

1. **SlackMCPBot**: Core class managing Slack events and message processing
2. **LLMClient**: Handles communication with LLM APIs (OpenAI, Groq, Anthropic)
3. **Server**: Manages communication with MCP servers
4. **Tool**: Represents available tools from MCP servers

When a message is received, the bot:
1. Sends the message to the LLM along with available tools
2. If the LLM response includes a tool call, executes the tool
3. Returns the result to the LLM for interpretation
4. Delivers the final response to the user

## Credits

This project is based on the [MCP Simple Chatbot example](https://github.com/modelcontextprotocol/python-sdk/tree/main/examples/clients/simple-chatbot).

## License

MIT License