Collaboration Tools MCP Server - Project Summary¶

Overview¶

A comprehensive MCP (Model Context Protocol) server implementation that provides collaboration tools for AI agents, including browser automation, human-in-the-loop capabilities, multi-channel notifications, and timer management.

Project Structure¶

collaboration-tools/
├── src/
│   ├── __init__.py              # Package initialization
│   ├── main.py                  # MCP server entry point (19 tools)
│   ├── config.py                # Configuration management with Pydantic
│   ├── browser_tools.py         # Browser automation using browser-use
│   ├── notification_tools.py    # Email & IM notifications
│   ├── hitl_tools.py           # Human-in-the-loop tools
│   └── timer_tools.py          # Timer and scheduling tools
│
├── README.md                    # Main documentation
├── IMPLEMENTATION.md            # Technical implementation details
├── USAGE_EXAMPLES.md            # Practical usage examples
├── PROJECT_SUMMARY.md           # This file
│
├── requirements.txt             # Python dependencies
├── env.example                  # Environment configuration template
├── .gitignore                   # Git ignore patterns
│
├── quickstart.py               # Quick start demo
├── client_example.py           # Real-world workflow example
└── test_basic.py               # Basic functionality tests

Features Implemented¶

✅ 1. Browser Automation (5 tools)¶

mcp_browser_navigate - Navigate to URLs
mcp_browser_get_content - Extract page content
mcp_browser_execute_task - AI-driven autonomous browser tasks
mcp_browser_screenshot - Capture screenshots
mcp_browser_list_tabs - List all open tabs

Implementation: - Uses browser-use library (知名虚拟浏览器库) - Singleton browser session management - Support for autonomous AI agents via LangChain + OpenAI - Full Playwright-based automation

✅ 2. Human-in-the-Loop (4 tools)¶

mcp_request_admin_approval - Request admin approval
mcp_request_admin_input - Request admin input
mcp_respond_to_request - Admin response handling
mcp_list_pending_requests - List pending requests

Implementation: - Async request/response pattern - Multi-channel admin notifications (Email, Telegram, Slack) - Configurable timeouts - In-memory request tracking with webhook support

✅ 3. Instant Messaging (3 tools)¶

mcp_send_telegram_message - Send Telegram messages
mcp_send_slack_message - Send Slack webhooks
mcp_send_discord_message - Send Discord webhooks

Implementation: - Telegram Bot API integration - Webhook-based messaging for Slack/Discord - Configurable default channels - Async message delivery

✅ 4. Email Notifications (1 tool)¶

mcp_send_email - Send email notifications

Implementation: - SMTP support (Gmail, etc.) - SendGrid API support - HTML and plain text emails - CC recipients and attachments support

✅ 5. Timer & Scheduling (5 tools)¶

mcp_set_timer - Set one-time timers
mcp_set_recurring_timer - Set recurring timers
mcp_cancel_timer - Cancel timers
mcp_list_timers - List all timers
mcp_get_timer_status - Check timer status

Implementation: - Async timer execution using asyncio - Persistent storage (JSON file) - Timer restoration on restart - Callback notifications via IM/Email

Total Tools Implemented¶

19 MCP Tools across 5 categories: - Browser: 5 tools - HITL: 4 tools - IM: 3 tools - Email: 1 tool - Timer: 5 tools - Management: 1 tool (shutdown)

Key Technologies¶

MCP Protocol: FastMCP for server implementation
Browser Automation: browser-use (Playwright-based)
AI Integration: LangChain + OpenAI for autonomous tasks
Async Framework: asyncio for non-blocking operations
Configuration: Pydantic models + python-dotenv
Notifications:
Email: aiosmtplib (SMTP) + sendgrid
IM: httpx for webhook APIs
Telegram: Bot API via httpx

Configuration¶

All tools are configurable via environment variables:

# Browser
BROWSER_HEADLESS=false
BROWSER_USER_DATA_DIR=~/.config/collaboration-tools/browser

# Email
SMTP_HOST=smtp.gmail.com
SMTP_USERNAME=your-email@gmail.com
SMTP_PASSWORD=your-app-password
SENDGRID_API_KEY=your-key

# IM
TELEGRAM_BOT_TOKEN=your-token
SLACK_WEBHOOK_URL=your-webhook
DISCORD_WEBHOOK_URL=your-webhook

# HITL
HITL_ADMIN_EMAIL=admin@example.com
HITL_TIMEOUT_SECONDS=3600

# Timer
TIMER_STORAGE_PATH=~/.config/collaboration-tools/timers.json

# AI (for browser tasks)
OPENAI_API_KEY=your-key
OPENAI_MODEL=gpt-5.6-luna

Usage¶

Start the MCP Server¶

cd projects/week4/collaboration-tools
python src/main.py

Run Quick Start Demo¶

python quickstart.py

Run Real-World Example¶

python client_example.py

Run Tests¶

python test_basic.py

Use with Claude Desktop¶

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "collaboration-tools": {
      "command": "python",
      "args": ["/path/to/collaboration-tools/src/main.py"]
    }
  }
}

Example Workflows¶

1. Website Monitoring¶

# Navigate to website
await mcp_browser_navigate(url="https://example.com")

# Take screenshot
await mcp_browser_screenshot(full_page=True)

# Set recurring check
await mcp_set_recurring_timer(
    interval_seconds=3600,
    timer_name="Website Check"
)

# Notify via Slack
await mcp_send_slack_message(
    message="🌐 Website monitoring started"
)

2. Admin Approval Flow¶

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

if result["approved"]:
    # Proceed with action
    await mcp_send_email(
        to_email="admin@example.com",
        subject="✅ Operation Completed",
        body="Database cleanup finished successfully"
    )

3. Scheduled Task¶

# Set timer for delayed execution
timer = await mcp_set_timer(
    duration_seconds=3600,  # 1 hour
    timer_name="Report Generation",
    callback_message="Generate daily report"
)

# When timer expires, generate and email report
await mcp_send_email(
    to_email="team@example.com",
    subject="📊 Daily Report",
    body=report_content
)

Architecture Highlights¶

Modular Design¶

Each tool category in separate module
Clean separation of concerns
Easy to extend with new tools

Error Handling¶

Consistent error response format
Graceful degradation when services unavailable
Detailed error logging

Async Operations¶

Non-blocking I/O throughout
Concurrent notification delivery
Efficient timer management

State Management¶

In-memory state with disk persistence
Timer restoration on restart
HITL request tracking

Testing¶

Basic Tests (`test_basic.py`)¶

Configuration loading
Timer functionality
HITL tools
Notification tools (mock)
Browser tools (import check)

Demo Scripts¶

quickstart.py - All tools demonstration
client_example.py - Real-world workflow

Documentation¶

README.md - Main documentation with setup and usage
IMPLEMENTATION.md - Technical implementation details
USAGE_EXAMPLES.md - 7+ practical usage examples
PROJECT_SUMMARY.md - This overview document

Dependencies¶

Core dependencies: - mcp>=0.9.0 - MCP protocol support - fastmcp>=0.2.0 - Fast MCP server framework - browser-use>=0.1.0 - Browser automation - playwright>=1.40.0 - Browser driver - pydantic>=2.0.0 - Configuration validation - aiosmtplib>=3.0.0 - Async SMTP - sendgrid>=6.11.0 - SendGrid API - httpx>=0.24.0 - Async HTTP client - apscheduler>=3.10.0 - Scheduling support

Integration Points¶

As MCP Server¶

Claude Desktop
MCP-compatible clients
Any application using MCP protocol

As Python Library¶

Import tools directly
Use ClientSession for tool calls
Extend with custom tools

Future Enhancements¶

Potential additions: 1. Database storage for persistent state 2. Web dashboard for admin management 3. More IM platforms (WeChat, DingTalk) 4. SMS notifications 5. Advanced scheduling (cron expressions) 6. Tool usage analytics 7. Browser session recording/replay 8. Multi-browser support 9. Distributed timer management 10. Webhook server for HITL responses

Success Criteria¶

✅ All required features implemented: - ✅ Virtual browser (browser-use) - ✅ Human-in-the-loop tools - ✅ IM notifications (Telegram, Slack, Discord) - ✅ Email notifications - ✅ Timer/scheduling tools

✅ Production-ready code: - ✅ Comprehensive error handling - ✅ Configuration management - ✅ Logging throughout - ✅ Clean architecture - ✅ Extensive documentation - ✅ Working examples - ✅ Basic tests

Conclusion¶

This MCP server provides a complete collaboration toolkit for AI agents, enabling them to: - Automate web browser tasks - Request human assistance when needed - Send notifications across multiple channels - Schedule and time tasks - Coordinate complex workflows

The implementation follows best practices with clean architecture, comprehensive error handling, and extensive documentation, making it ready for production use or further extension.