🚀 Collaboration Tools MCP Server¶

Start Here - Complete guide to the Collaboration Tools MCP Server implementation

📋 What Is This?¶

A production-ready Model Context Protocol (MCP) server that provides 19 collaboration tools for AI agents across 5 categories:

✅ Implemented Features¶

🌐 Browser Automation (5 tools)¶

Virtual browser using browser-use library (知名虚拟浏览器库)
Navigate websites, extract content, take screenshots
AI-powered autonomous browser tasks
Multi-tab management

👤 Human-in-the-Loop (4 tools)¶

Request admin approval for sensitive operations
Request human input with timeout handling
Multi-channel admin notifications
Pending request management

💬 Instant Messaging (3 tools)¶

Telegram bot integration
Slack webhook messaging
Discord webhook messaging

📧 Email Notifications (1 tool)¶

SMTP support (Gmail, etc.)
SendGrid API support
HTML emails with attachments

⏰ Timer & Scheduling (5 tools)¶

One-time timers
Recurring timers
Timer cancellation and management
Persistent timer storage
Callback notifications

🎯 Quick Start¶

1. Installation¶

cd projects/week4/collaboration-tools

# Install dependencies
pip install -r requirements.txt

# Install Playwright browsers
playwright install chromium

# Configure environment
cp env.example .env
# Edit .env with your credentials

2. Run Demo¶

# Quick start demo (all tools)
python quickstart.py

# Real-world example
python client_example.py

# Basic tests
python test_basic.py

3. Start MCP Server¶

# Run as MCP server
python src/main.py

# Use with Claude Desktop (add to config)
# See README.md for configuration

📁 Project Structure¶

collaboration-tools/                    (Total: 2,331 lines of Python code)
│
├── 📘 Documentation (80KB total)
│   ├── 00_START_HERE.md              ← You are here
│   ├── README.md                      (6.7KB) Main documentation
│   ├── IMPLEMENTATION.md              (7.3KB) Technical details
│   ├── ARCHITECTURE.md                (23KB)  System architecture
│   ├── USAGE_EXAMPLES.md              (14KB)  7+ practical examples
│   └── PROJECT_SUMMARY.md             (9.2KB) Project overview
│
├── 🔧 Configuration
│   ├── requirements.txt               19 dependencies
│   ├── env.example                    Configuration template
│   └── .gitignore                     Git ignore patterns
│
├── 🎯 Demo & Testing
│   ├── quickstart.py                  (6.1KB) Quick start demo
│   ├── client_example.py              (7.2KB) Real-world workflow
│   └── test_basic.py                  (4.7KB) Basic tests
│
└── 📦 Source Code (src/)
    ├── main.py                        (11KB)  MCP server (19 tools)
    ├── config.py                      (3.5KB) Configuration management
    ├── browser_tools.py               (8.3KB) Browser automation
    ├── notification_tools.py          (11KB)  Email & IM notifications
    ├── hitl_tools.py                  (11KB)  Human-in-the-loop
    └── timer_tools.py                 (14KB)  Timer management

🛠️ Technology Stack¶

Component	Technology
MCP Server	FastMCP (mcp>=0.9.0)
Browser Automation	browser-use + Playwright
AI Agent	LangChain + OpenAI
Email	aiosmtplib (SMTP) + SendGrid
IM	httpx (Webhooks) + Telegram Bot API
Async	asyncio (Python 3.11+)
Config	Pydantic + python-dotenv
Scheduling	apscheduler + asyncio

📚 Documentation Guide¶

For Getting Started¶

00_START_HERE.md (this file) - Overview and quick start
README.md - Installation, configuration, and basic usage

For Implementation¶

ARCHITECTURE.md - System architecture and data flows
IMPLEMENTATION.md - Technical implementation details

For Usage¶

USAGE_EXAMPLES.md - 7+ practical usage examples
quickstart.py - Runnable demo of all features
client_example.py - Real-world workflow example

For Summary¶

PROJECT_SUMMARY.md - Complete project overview

🎨 Key Features¶

1. Browser Automation with AI¶

# Autonomous browser task using AI
await mcp_browser_execute_task(
    task="Search for AI agent tutorials on Google and extract top 5 results",
    max_steps=30
)

2. Human-in-the-Loop Workflow¶

# Request approval with timeout
result = await mcp_request_admin_approval(
    request_message="Delete 1000 database records?",
    urgent=True,
    timeout_seconds=300
)

if result["approved"]:
    # Proceed with action
    perform_deletion()

3. Multi-Channel Notifications¶

# Send alert via all channels
await mcp_send_email(to_email="admin@example.com", ...)
await mcp_send_slack_message(message="🚨 Alert!")
await mcp_send_telegram_message(message="Alert!")
await mcp_send_discord_message(message="Alert!")

4. Timer & Scheduling¶

# Set timer for delayed execution
timer = await mcp_set_timer(
    duration_seconds=3600,
    callback_message="Time to check website"
)

# Recurring timer
await mcp_set_recurring_timer(
    interval_seconds=300,  # Every 5 minutes
    max_occurrences=10
)

📊 Statistics¶

Total Files: 17 (7 Python modules + 10 docs/config)
Lines of Code: 2,331 (Python)
Documentation: ~80KB
MCP Tools: 19 tools across 5 categories
Dependencies: 19 packages
Test Coverage: Basic tests included

🔐 Security Features¶

✅ Environment-based configuration (no hardcoded secrets)
✅ .env file excluded from git
✅ Isolated browser user data directory
✅ HITL timeout and multi-channel verification
✅ Graceful error handling throughout
✅ Audit trail for admin approvals

🚦 Usage Patterns¶

Pattern 1: Website Monitoring¶

navigate → screenshot → set_recurring_timer → notify_via_slack

Pattern 2: Admin Approval Flow¶

request_approval → wait_for_response → notify_decision → execute_action

Pattern 3: Scheduled Task¶

set_timer → browser_task → extract_data → send_email_report

Pattern 4: Multi-Channel Alert¶

critical_event → [email, slack, telegram, discord] → admin_approval

📖 Next Steps¶

To Use This Project:¶

Read Documentation
Start with README.md for setup
Check USAGE_EXAMPLES.md for practical examples
Review ARCHITECTURE.md for technical details
Configure Environment
Copy env.example to .env
Add your API keys and credentials
Configure notification channels
Run Demos
python quickstart.py - See all tools in action
python client_example.py - Real-world workflow
python test_basic.py - Verify installation
Start Using
Run as MCP server: python src/main.py
Use with Claude Desktop or custom client
Integrate into your AI agent application

To Extend This Project:¶

Add New Tools: Create new functions in existing modules
Add New Channels: Extend notification_tools.py
Add Storage: Replace in-memory state with database
Add Dashboard: Build web UI for admin management
Add Analytics: Track tool usage and performance

🆘 Troubleshooting¶

Browser Issues¶

# Reinstall Playwright
playwright install chromium --force

Email Issues¶

Use Gmail App Passwords (not regular password)
Check SMTP port and host settings

Import Errors¶

# Reinstall dependencies
pip install -r requirements.txt --force-reinstall

Permission Issues¶

# Ensure config directory is writable
mkdir -p ~/.config/collaboration-tools
chmod 755 ~/.config/collaboration-tools

📞 Support¶

Documentation: Check all .md files in this directory
Examples: See quickstart.py and client_example.py
Tests: Run test_basic.py to verify functionality
Issues: Review error messages and logs

🎓 Learning Path¶

Beginner: Run quickstart.py and read README.md
Intermediate: Study USAGE_EXAMPLES.md and modify examples
Advanced: Review ARCHITECTURE.md and extend functionality

✅ Implementation Checklist¶

✅ Virtual browser (browser-use library)
✅ Human-in-the-loop tools
✅ IM notifications (Telegram, Slack, Discord)
✅ Email notifications (SMTP + SendGrid)
✅ Timer and scheduling tools
✅ Configuration management
✅ Error handling and logging
✅ Comprehensive documentation
✅ Working examples and demos
✅ Basic test suite
✅ Clean architecture
✅ Production-ready code

🌟 Highlights¶

Production-Ready: Comprehensive error handling and logging
Well-Documented: 80KB+ of documentation
Modular Design: Easy to extend and maintain
Real Examples: Working demos and use cases
Best Practices: SOLID principles, clean code, async patterns

📝 License¶

MIT License - See project root for details

Ready to start? → Continue to README.md for detailed setup instructions!