一个简单的Slack机器人，使用模型上下文协议（MCP）来增强其外部工具的能力。

特色

• AI驱动助手：利用LLM功能响应频道和私信中的消息
• MCP 集成：完全访问 SQLite 数据库和网页取用等 MCP 工具
• 多LLM支持：支持OpenAI、Groq和Anthropic模型
• 应用主页标签页：显示可用工具和使用信息

布置

1.创建一个 Slack 应用

1. 进入 api.slack.com/apps 并点击“创建新应用”
2. 选择“从应用清单”，选择你的工作区
3. 将mcp_simple_slackbot/manifest.yaml的内容复制到清单编辑器中
4. 创建应用并安装到你的工作区
5. 在“基本信息”部分，向下滚动至“应用级代币”
6. 点击“生成令牌和范围”，然后：

• 输入类似“mcp-assistant”这样的名称
• 添加 connections:write 作用域
• 点击“生成”

7. 注意你两人：

• 机器人令牌（xoxb-...），见于“OAuth & Permissions”
• 你刚刚生成的应用令牌（xapp-...）

2.安装依赖

3.配置环境变量

创建一个“。

MCP Simple Slackbot

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

Features

• 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 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

# 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

# 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