by hi5d
Find AMC showtimes, buy movie tickets online, theater seat selection, and book instantly with real-time availability via
Enables conversational AI assistants to help users book movie tickets at AMC Theatres, from finding showtimes to completing seat selection and payment.
AMC MCP Server is a community-built MCP server published by hi5d that provides AI assistants with tools and capabilities via the Model Context Protocol. Find AMC showtimes, buy movie tickets online, theater seat selection, and book instantly with real-time availability via It is categorized under other, developer tools.
You can install AMC MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
AMC MCP Server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
AMC MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired AMC MCP Server into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: AMC MCP Server surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend AMC MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Useful MCP listing: AMC MCP Server is the kind of server we cite when onboarding engineers to host + tool permissions.
AMC MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: AMC MCP Server surfaces stars and publisher context so we could sanity-check maintenance before adopting.
According to our notes, AMC MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired AMC MCP Server into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
AMC MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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An Model Context Protocol (MCP) server that provides a comprehensive movie booking experience for AMC Theatres. This server enables conversational AI assistants to help users discover movies, find showtimes, book seats, and process payments through a simple API interface.
git clone <repository-url>
cd amc-mcp
pip install -r requirements.txt
pip install -e .
python -m amc_mcp.fastmcp_server
docker-compose up --build
docker build -t amc-mcp .
docker run -it amc-mcp
Returns a list of movies currently showing in a given location.
Input:
{
"location": "Boston, MA"
}
Output:
{
"location": "Boston, MA",
"movies": [
{
"movie_id": "mv001",
"title": "Dune: Part Two",
"rating": "PG-13",
"duration": 166,
"genre": "Sci-Fi/Action",
"description": "Paul Atreides unites with Chani..."
}
]
}
Suggests movies based on mood, genre, or preferences.
Input:
{
"genre": "action",
"mood": "exciting"
}
Output:
{
"criteria": {"genre": "action", "mood": "exciting"},
"recommendations": [...]
}
Fetches available showtimes for a specific movie and location.
Input:
{
"movie_id": "mv001",
"date": "2025-10-28",
"location": "Boston, MA"
}
Output:
{
"movie": {"id": "mv001", "title": "Dune: Part Two"},
"date": "2025-10-28",
"location": "Boston, MA",
"showtimes": [
{
"showtime_id": "st001",
"theater_name": "AMC Boston Common 19",
"theater_address": "175 Tremont Street",
"time": "14:00",
"format": "IMAX",
"price": 18.50
}
]
}
Displays available and reserved seats for a specific showtime.
Input:
{
"showtime_id": "st001"
}
Output:
{
"showtime_id": "st001",
"movie": "Dune: Part Two",
"theater": "AMC Boston Common 19",
"date": "2025-10-28",
"time": "14:00",
"seat_map": [
{
"seat_number": "A5",
"row": "A",
"column": 5,
"is_available": true,
"price_tier": "Standard",
"price": 18.50
}
]
}
Reserves selected seats for the user.
Input:
{
"showtime_id": "st001",
"seats": ["A5", "A6"],
"user_id": "user123"
}
Output:
{
"booking_id": "booking-uuid",
"status": "pending",
"movie": "Dune: Part Two",
"theater": "AMC Boston Common 19",
"date": "2025-10-28",
"time": "14:00",
"seats": ["A5", "A6"],
"total_price": 37.00
}
Handles simulated payment transaction.
Input:
{
"booking_id": "booking-uuid",
"payment_method": "card",
"amount": 37.00
}
Output:
{
"payment_id": "payment-uuid",
"payment_status": "success",
"booking_id": "booking-uuid",
"receipt_url": "https://amc.com/receipts/payment-uuid",
"confirmation": {
"movie": "Dune: Part Two",
"theater": "AMC Boston Common 19",
"date": "2025-10-28",
"time": "14:00",
"seats": ["A5", "A6"],
"total_paid": 37.00
}
}
Here's how a typical movie booking conversation would work:
User: "Find an action movie near me tonight."
get_now_showing + get_recommendationsUser: "Book two seats for Dune: Part Two at 8 PM."
get_showtimes → get_seat_map → book_seatsUser: "Pay with my card."
process_paymentamc-mcp/
├── src/
│ └── amc_mcp/
│ ├── __init__.py
│ └── server.py # Main MCP server implementation
├── data/
│ ├── movies.json # Movie catalog
│ ├── theaters.json # Theater locations
│ ├── showtimes.json # Showtime schedules
│ └── seats.json # Seat maps by showtime
├── config/
│ └── nginx.conf # Web server configuration
├── Dockerfile # Container configuration
├── docker-compose.yml # Multi-service orchestration
├── requirements.txt # Python dependencies
├── pyproject.toml # Package configuration
└── README.md # This file
{
"movie_id": str,
"title": str,
"rating": str, # PG, PG-13, R, etc.
"duration": int, # Minutes
"genre": str,
"description": str,
"poster_url": str
}
{
"theater_id": str,
"name": str,
"address": str,
"city": str,
"state": str,
"zip_code": str
}
{
"showtime_id": str,
"movie_id": str,
"theater_id": str,
"date": str, # YYYY-MM-DD
"time": str, # HH:MM
"format": str, # Standard, IMAX, 3D, Dolby
"price": float
}
Edit data/movies.json to add new movies:
{
"movie_id": "mv011",
"title": "New Movie Title",
"rating": "PG-13",
"duration": 120,
"genre": "Action",
"description": "Description of the movie...",
"poster_url": "https://example.com/poster.jpg"
}
Edit data/theaters.json:
{
"theater_id": "th011",
"name": "AMC New Location 15",
"address": "123 Main Street",
"city": "New City",
"state": "NY",
"zip_code": "12345"
}
Edit data/showtimes.json and data/seats.json to add new showtimes and corresponding seat maps.
You can test individual tools using the MCP inspector or by connecting to any MCP-compatible client.
PYTHONPATH: Set to /app/src for proper module resolutionPYTHONUNBUFFERED: Set to 1 for real-time loggingMCP_LOG_LEVEL: Set logging level (DEBUG, INFO, WARNING, ERROR)The server runs in a lightweight Python 3.11 container with:
This is a mock implementation for demonstration purposes. In production:
git checkout -b feature/new-featuregit commit -am 'Add new feature'git push origin feature/new-featureThis project is licensed under the MIT License - see the LICENSE file for details.
For questions, issues, or feature requests:
Happy movie booking! 🍿🎬
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.