Which integrations are available for this server?

Enables secure credential management for production deployments on AWS ECS infrastructure. Integrates GPT-4o-mini for AI-powered question answering, technical explanations, autonomous task planning and execution, and conversation analysis. Provides comprehensive Slack workspace management including sending messages, retrieving channel history, searching messages, creating channels, analyzing conversations with AI, and real-time Socket Mode integration for instant bot responses to mentions.

How do I use Slack MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Slack MCP Server summarize yesterday's #team-meeting and research the new framework mentioned" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🚀 Slack MCP Server

Transform your Slack workspace into an intelligent AI assistant with real-time responses

GitHub stars GitHub license Python 3.11+ FastMCP

A production-ready Slack MCP Server that combines real-time Socket Mode integration with AI-powered tools. Built with FastMCP 2.8.0, this project provides 18+ tools for Claude Desktop and features an autonomous AI assistant that can plan and execute complex tasks.

⭐ What Makes This Special?

🤖 Real-Time Socket Mode Bot - Instant responses to Slack mentions (< 2 seconds)
🧠 Autonomous AI Agent - AI that plans its own execution strategies
🔧 18+ MCP Tools - Complete Slack workspace management via Claude Desktop
🌐 Web Research Integration - Real-time web search with Tavily API
⚡ Production Ready - Docker + AWS ECS deployment included
🛡️ Enterprise Security - AWS Secrets Manager integration

🎥 Demo

claude_text

slackchat

@MCP Bot analyze yesterday's #product-meeting and research mentioned technologies

↓ *2 seconds later* ↓

🤖 Analysis Complete! Found discussion about React 18, TypeScript 5.0, and Docker. 📊 Meeting Summary: [Detailed analysis...] 🔍 Technology Research: [Latest developments...] 📝 Action Items: [Extracted tasks...]

## 🚀 Quick Start (5 Minutes) ### 1. Clone & Install ```bash git clone https://github.com/bahakizil/slack_mcp.git cd slack_mcp pip install -r requirements.txt

2. Configure API Keys

Create .env file:

SLACK_BOT_TOKEN=xoxb-your-bot-token # From api.slack.com/apps SLACK_APP_TOKEN=xapp-your-app-token # For Socket Mode (required!) OPENAI_API_KEY=sk-your-openai-key # From platform.openai.com TAVILY_API_KEY=tvly-your-tavily-key # From tavily.com (optional)

3. Start the Server

python main.py

Success Output:

🤖 Starting Slack MCP Server with Real-time Mode ⚡ Socket Mode: Enabled (Real-time auto-responses) ✅ Bot ready for mentions: @MCP Bot INFO: Uvicorn running on http://0.0.0.0:8003

4. Test in Slack

@MCP Bot hello!

🤖 Hello! I'm your AI assistant. How can I help you today? 😊

🎉 Your AI assistant is now live!

🔧 Complete Feature Set

📱 Slack Management (6 Tools)

Tool	Description	Example
`send_slack_message`	Send messages to channels	`send_slack_message("general", "Hello team!")`
`get_slack_channels`	List all workspace channels	`get_slack_channels()`
`get_slack_messages`	Retrieve channel history	`get_slack_messages("engineering", limit=50)`
`search_slack_messages`	Search across workspace	`search_slack_messages("deployment")`
`create_slack_channel`	Create new channels	`create_slack_channel("new-project")`
`analyze_slack_conversation`	AI-powered analysis	`analyze_slack_conversation("planning", "summary")`

🤖 AI Intelligence (3 Tools)

Tool	Description	Use Case
`ask_ai`	Ask questions to GPT-4o-mini	Technical explanations, brainstorming
`autonomous_assistant`	🌟 Self-planning AI agent	Complex multi-step tasks
Socket Mode Bot	Real-time auto-responses	Instant Slack interaction

🌐 Web Research (3 Tools)

Tool	Description	Example
`search_web`	General web search	`search_web("latest AI developments 2024")`
`search_news`	News-specific search	`search_news("tech industry", days=7)`
`research_topic`	Deep research analysis	`research_topic("microservices architecture")`

🔗 MCP Server Management (6 Tools)

Tool	Description	Purpose
`add_mcp_server`	Connect external MCP servers	Extend functionality
`list_mcp_servers`	Show all configured servers	Server management
`connect_mcp_server`	Establish connections	Activate external tools
`disconnect_mcp_server`	Close connections	Resource management
`list_external_tools`	Browse external tools	Tool discovery
`call_external_tool`	Execute external tools	Cross-server operations

🧠 Autonomous AI Assistant

The autonomous assistant is the crown jewel of this project. It can:

📋 Plan its own execution - No pre-programmed workflows
🔍 Discover tools dynamically - Adapts to available resources
🔄 Self-reflect and optimize - Learns from execution history
📊 Synthesize multi-source data - Combines Slack + web + AI knowledge

Example: Complex Task Execution

autonomous_assistant( request="Analyze all engineering discussions this week, research mentioned technologies, and create a strategic report", send_to_slack=True )

What happens behind the scenes:

🔍 Discovery: Finds available Slack channels and tools
🧠 Planning: AI creates step-by-step execution plan
⚡ Execution: Runs multiple tools in sequence
🔬 Research: Web searches for mentioned technologies
📊 Synthesis: Combines all data into comprehensive report
📨 Delivery: Posts results to Slack automatically

🖥️ Claude Desktop Integration

Configuration

Add to claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "slack-ai-assistant": { "command": "python", "args": ["/full/path/to/main.py"], "env": { "SLACK_BOT_TOKEN": "xoxb-your-actual-bot-token-here", "SLACK_APP_TOKEN": "xapp-your-actual-app-token-here", "OPENAI_API_KEY": "sk-your-actual-openai-api-key-here", "TAVILY_API_KEY": "tvly-your-actual-tavily-api-key-here" } } } }

Usage Examples in Claude Desktop

Meeting Analysis:

Analyze yesterday's #product-meeting channel and summarize key decisions, action items, and sentiment.

Tech Research + Slack Update:

Research current best practices for microservices deployment and share findings in #engineering channel.

Multi-Channel Intelligence:

Compare discussions across #design, #engineering, and #marketing channels this week. Identify common themes and potential collaboration opportunities.

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐ │ AUTONOMOUS AI SYSTEM │ ├─────────────────────────────────────────────────────────────┤ │ ┌─────────────────┐ ┌─────────────────┐ │ │ │ SLACK MCP │ │ TAVILY MCP │ │ │ │ SERVER │ │ SERVER │ │ │ │ Port: 8001 │ │ Port: 8002 │ │ │ │ │ │ │ │ │ │ • Slack Tools │ │ • Web Search │ │ │ │ • AI Tools │ │ • News Search │ │ │ │ • Orchestration │ │ • Research │ │ │ └─────────────────┘ └─────────────────┘ │ │ │ │ │ │ └───────────┐ ┌───────┘ │ │ │ │ │ │ ┌─────────────────┐ │ │ │ AUTONOMOUS │ │ │ │ AGENT │ │ │ │ │ │ │ │ • Tool Discovery│ │ │ │ • Plan Creation │ │ │ │ • Execution │ │ │ │ • Synthesis │ │ │ └─────────────────┘ │ │ │ │ │ ┌─────────────────┐ │ │ │ OPENAI GPT │ │ │ │ (gpt-4.1-nano) │ │ │ └─────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ## ☁️ Production Deployment ### 🐳 Docker Deployment ```bash # Build and run locally docker build -t slack-mcp-server . docker run -p 8003:8003 --env-file .env slack-mcp-server

diagram

🚀 AWS ECS Deployment (Production)

# Automated deployment ./deploy.sh

Includes:

✅ Auto-scaling based on traffic
✅ Health checks and monitoring
✅ AWS Secrets Manager for secure credential management
✅ Application Load Balancer for high availability
✅ CloudWatch logging for debugging

📊 Production Features

Zero downtime deployments
Horizontal scaling (multiple instances)
SSL/TLS termination
Custom domain support
Monitoring & alerting

🛡️ Security Best Practices

🔐 Credential Management

Environment variables for local development
AWS Secrets Manager for production
No hardcoded secrets in code
Git ignore for sensitive files

🛠️ Error Handling

Graceful degradation when services are down
Retry logic for transient failures
User-friendly error messages
Comprehensive logging

📈 Performance

Socket Mode eliminates polling overhead
Async processing for non-blocking operations
Connection pooling for API efficiency
Response time < 2 seconds

🎯 Real-World Use Cases

👥 For Teams

Daily standups: "Summarize yesterday's progress across all channels"
Sprint planning: "Analyze #product-backlog and prioritize by urgency"
Knowledge sharing: "Research best practices for the technologies we discussed"

🏢 For Managers

Team sentiment: "Analyze team mood and engagement this week"
Progress tracking: "Extract action items from all project channels"
Strategic insights: "Compare our technical discussions with industry trends"

🔬 For Developers

Tech research: "Find the latest updates on frameworks we're using"
Code review insights: "Analyze #code-review discussions for common issues"
Learning automation: "Create weekly tech digest from our discussions"

📚 Documentation

📖 Quick Start Guide - 5-minute setup
🏗️ Complete Technical Guide - In-depth architecture
🚀 Deployment Guide - Production deployment
✅ Project Status - Current implementation status

🔧 Requirements

Slack App Setup

Required Bot Token Scopes:

channels:read, channels:history, chat:write, groups:read, groups:history, users:read, search:read, channels:manage

API Keys

Slack Bot Token (xoxb-...) - Required
Slack App Token (xapp-...) - Required for Socket Mode
OpenAI API Key (sk-...) - Required for AI features
Tavily API Key (tvly-...) - Optional for web search

System Requirements

Python 3.11+
4GB RAM (minimum)
Network access to Slack, OpenAI, and Tavily APIs

🔍 Troubleshooting

Common Issues

1. Socket Mode not working

# Check App Token configuration echo $SLACK_APP_TOKEN # Should start with xapp- # Verify Socket Mode is enabled in Slack app settings # Go to api.slack.com/apps → Your App → Socket Mode → Enable

2. Bot not responding

# Check bot token echo $SLACK_BOT_TOKEN # Should start with xoxb- # Verify terminal shows: # ✅ Socket Mode: Enabled (Real-time auto-responses)

3. Claude Desktop not loading

# Check config file path and restart Claude Desktop # Verify Python is in PATH: python --version

Debug Mode

# Enable detailed logging export DEBUG=true export LOG_LEVEL=DEBUG python main.py

🤝 Contributing

We welcome contributions! Here's how to get started:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

🎯 Areas for Contribution

🔧 New MCP tools - Extend functionality
🌐 Language support - Add more languages
📊 Analytics features - Advanced reporting
🔌 Integration templates - More external services
📚 Documentation - Examples and tutorials

📄 License

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

🙋 Support & Community

💬 Issues: GitHub Issues
📖 Documentation: Check the docs/ folder
🐛 Bug Reports: Use issue templates
💡 Feature Requests: Community-driven roadmap

🌟 Acknowledgments

FastMCP - Amazing MCP framework
Slack SDK - Robust Slack integration
OpenAI - Powerful AI capabilities
Tavily - Excellent web search API

🚀 Ready to Get Started?

⭐ Star this repository if you find it useful
🍴 Fork it to start customizing
📥 Clone it and follow the 5-minute setup
🤖 Mention your bot in Slack and watch the magic happen!

Built with ❤️ by

Transform your Slack workspace into an AI-powered productivity hub today! 🚀