Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AMC MCP Server Find showtimes for Dune: Part Two in Chicago this weekend."
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.
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.
Movie Discovery: Browse currently showing movies and get personalized recommendations
Showtime Lookup: Find available showtimes by location, date, and movie
Seat Selection: View interactive seat maps and check availability
Booking Management: Reserve seats with real-time availability checking
Payment Processing: Handle mock payment transactions with confirmation receipts
Multi-location Support: Search across multiple AMC theater locations
Related MCP server: AMC MCP Server
Python 3.8+
Docker (optional, for containerized deployment)
Clone the repository:
git clone <repository-url>
cd amc-mcpInstall dependencies:
pip install -r requirements.txtInstall the package:
pip install -e .Run the server:
python -m amc_mcp.fastmcp_serverBuild and run with Docker Compose:
docker-compose up --buildOr build and run manually:
docker build -t amc-mcp .
docker run -it amc-mcpReturns 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."
Server calls: get_now_showing + get_recommendations
Returns: List of action movies with showtimes
User: "Book two seats for Dune: Part Two at 8 PM."
Server calls: get_showtimes ā get_seat_map ā book_seats
Returns: Seat selection and booking confirmation
User: "Pay with my card."
Server calls: process_payment
Returns: Payment confirmation with digital receipt
amc-mcp/
āāā src/
ā āāā amc_mcp/
ā āāā __init__.py
ā āāā server.py # Main MCP server implementation
āāā data/
ā āāā movies.json # Movie catalog
ā āāā theaters.json # Theater locations
ā āāā showtimes.json # Showtime schedules
ā āāā seats.json # Seat maps by showtime
āāā config/
ā āāā nginx.conf # Web server configuration
āāā Dockerfile # Container configuration
āāā docker-compose.yml # Multi-service orchestration
āāā requirements.txt # Python dependencies
āāā pyproject.toml # Package configuration
āāā 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.
Configure Claude Desktop to connect to your MCP server
Use natural language to test the booking flow
Example: "Find me a sci-fi movie showing tonight in Boston"
PYTHONPATH: Set to /app/src for proper module resolution
PYTHONUNBUFFERED: Set to 1 for real-time logging
MCP_LOG_LEVEL: Set logging level (DEBUG, INFO, WARNING, ERROR)
The server runs in a lightweight Python 3.11 container with:
Non-root user for security
Health checks for monitoring
Volume mounts for data persistence
Network isolation
This is a mock implementation for demonstration purposes. In production:
Payment Processing: Integrate with real payment gateways (Stripe, PayPal)
Authentication: Add user authentication and authorization
Data Validation: Implement comprehensive input validation
Rate Limiting: Add API rate limiting
Encryption: Use HTTPS and encrypt sensitive data
Database: Replace JSON files with a real database
Logging: Implement structured logging and monitoring
Real AMC API Integration: Connect to actual AMC Theatres API
User Accounts: Persistent user profiles and booking history
Group Bookings: Support for multiple users booking together
Loyalty Programs: AMC Stubs integration
Mobile Tickets: Generate QR codes for mobile entry
Seat Recommendations: AI-powered optimal seat suggestions
Price Alerts: Notify users of discounts and promotions
Social Features: Share movie plans with friends
Accessibility: ADA-compliant seat selection
Multi-language: International language support
Fork the repository
Create a feature branch: git checkout -b feature/new-feature
Make your changes and add tests
Commit your changes: git commit -am 'Add new feature'
Push to the branch: git push origin feature/new-feature
Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
For questions, issues, or feature requests:
Create an issue in the GitHub repository
Check the documentation for common solutions
Review the example conversation flows
Happy movie booking! šæš¬
Reserves selected seats for the user.
| Name | Required | Description | Default |
|---|---|---|---|
| seats | Yes | List of seat numbers (e.g., ["A5", "A6"]) | |
| user_id | Yes | User identifier | |
| showtime_id | Yes | Showtime ID (e.g., "st001") |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Reserves' implies a mutation, but it does not clarify what happens if seats are unavailable, whether the hold is temporary, whether a booking record is created, or any other side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It is concise and front-loaded, though slightly underspecified for the complexity of the operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the booking context and lack of annotations, the description is too minimal. It omits key context such as prerequisites (e.g., seat availability), interaction with payment, failure modes, and whether the reservation is temporary or permanent. The existence of an output schema does not compensate for missing behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions already cover all three parameters with examples and types. The tool description adds no additional parameter semantics, so it is sufficient but not enhanced.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Reserves selected seats for the user' uses a specific verb ('reserves') and identifies the resource ('selected seats') and actor ('the user'). It clearly distinguishes itself from read-only sibling tools like get_showtimes and get_seat_map, though it does not explicitly differentiate from process_payment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., checking seat availability via get_seat_map) or the intended flow with process_payment, leaving the usage context implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a list of movies currently showing in a given city or ZIP code.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | City, state or ZIP code (e.g., "Boston, MA") |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the basic behavior (returns a list by location) but does not disclose edge cases, response format, or potential errors. For a simple read operation, this is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 14-word sentence that is front-loaded and free of filler. Every word contributes to conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one parameter), a high-coverage schema, and an output schema to document returns, the description is largely complete. It lacks explicit sibling differentiation but is otherwise sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully documents the single parameter 'location' with a clear description and example, achieving 100% coverage. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Returns a list') and the resource ('movies currently showing') with a location filter. It distinguishes itself from siblings like get_showtimes (which is about showtimes) and get_recommendations (which is about recommendations).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving now-showing movies by location, but does not explicitly mention when to use this tool over alternatives like get_showtimes. No exclusions or alternative guidance is provided, leaving the agent to infer based on the tool name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Suggests movies based on mood, genre, or time preferences.
| Name | Required | Description | Default |
|---|---|---|---|
| mood | No | Mood description (optional, e.g., "exciting", "romantic") | |
| genre | No | Movie genre (optional, e.g., "action", "comedy") | |
| time_preference | No | Time of day preference (optional, e.g., "evening") |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only states the basic purpose and does not disclose any behavioral traits such as non-destructive nature, data source dependencies, or whether suggestions are limited to current showings. This is a minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no filler words, clearly front-loaded with the action and resource. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with three optional parameters and a provided output schema, the description, together with the rich schema, is sufficient for an agent to invoke it correctly. However, it does not elaborate on behavior when no parameters are provided, which is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover all three parameters (mood, genre, time_preference) with examples, so the schema provides full parameter semantics. The description itself adds no additional parameter information beyond what the schema already contains.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'suggests' with a clear resource 'movies' and distinguishes the tool from siblings like get_now_showing, get_showtimes, and booking tools by focusing on preference-based recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use the tool (when movie recommendations based on mood, genre, or time are needed). It does not explicitly name alternatives, but the context is clear and no exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Displays available and reserved seats for a specific showtime.
| Name | Required | Description | Default |
|---|---|---|---|
| showtime_id | Yes | Showtime ID (e.g., "st001") |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. The verb 'Displays' clearly signals a read-only operation, and mentioning 'available and reserved seats' gives insight into what the user will see. It does not state side effects, but the non-destructive intent is apparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that directly states the tool's purpose and scope. It is front-loaded with the action and object, containing zero wasteful words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple tool with one parameter, an output schema exists (so return values need not be explained), and the description fully covers the essential context: what it does and for what. No critical information is missing for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers the single parameter showtime_id with a clear description and example (100% coverage). The description only restates that it is for a specific showtime, adding no additional semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Displays') and resource ('available and reserved seats') for a specific showtime, clearly distinguishing this tool from siblings like get_showtimes (listing times) and book_seats (booking). It precisely explains what the tool does without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage in the context of viewing seat availability for a showtime, but it does not explicitly state when to use this versus alternatives or mention any prerequisites (e.g., 'use before booking'). There is no exclusion or alternative reference, so the guideline is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetches available showtimes for a specific movie and location.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date in YYYY-MM-DD format (e.g., "2025-10-28") | |
| location | Yes | City, state or ZIP code | |
| movie_id | Yes | Movie ID (e.g., "mv001") |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully carry the behavioral disclosure burden. It only states the core fetch action, providing no context on response format, empty results, or side effects. This is minimal disclosure beyond what the tool name already implies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with a clear verb and object. It is front-loaded and contains no filler words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple tool with fully described parameters and an output schema, the description captures the core function. However, it omits the date aspect from the description and does not mention error handling or empty results, though these are likely covered by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for all three parameters, so the baseline is 3. The description adds no extra parameter meaning and actually omits the required 'date' parameter, though the schema fully documents it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Fetches' with the resource 'available showtimes for a specific movie and location.' This clearly distinguishes it from sibling tools like get_now_showing (lists movies) or get_seat_map (seat availability).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (fetch showtimes for a movie/location) but does not explicitly state when to use it instead of siblings, nor any prerequisites or exclusions. It lacks guidance on process flow, such as first obtaining a movie_id via get_now_showing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Handles simulated payment transaction.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | Payment amount in USD | |
| booking_id | Yes | Booking ID from book_seats | |
| payment_method | Yes | Payment method (e.g., "card", "cash") |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The only behavioral disclosure is 'simulated', indicating no real charge occurs. With no annotations provided, the description carries the full burden, yet it fails to mention side effects, failure modes, idempotency, or any state changes, leaving significant behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no redundant wording, making it concise and easy to parse. However, it is terse to the point of vagueness, though this is a conciseness issue rather than a structural one.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the output schema exists and parameters are fully documented, the tool description lacks critical workflow context: how it fits with booking_id from book_seats, what happens after payment, and any constraints or requirements. For a payment tool, this is inadequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full descriptions for all three parameters (amount, booking_id, payment_method), giving 100% coverage. The description adds no additional meaning beyond the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource as a 'payment transaction' and notes it is simulated, which distinguishes it from the get_* and book_seats siblings. However, the verb 'handles' is vague and does not specify the exact action (e.g., process, charge, authorize), making the purpose only minimally clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, and no mention of prerequisites such as the booking_id coming from book_seats. The description implies it is part of a booking flow but does not state this explicitly or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a distinct stage of the movie booking flow: browsing movies (now_showing), personalized suggestions (recommendations), specific showtimes, seat selection, booking, and payment. There is little to no overlap, and descriptions clearly differentiate the tools.
All tool names follow a consistent verb_noun pattern with lowercase snake_case: get_now_showing, get_recommendations, get_showtimes, get_seat_map, book_seats, process_payment. The pattern is uniform and predictable.
With 6 tools, the server is well-scoped for a movie ticketing workflow. Each tool is necessary for the core journey from discovering movies to completing payment, and no redundant tools are present.
The tool set covers the full path from movie discovery to payment, but lacks optional lifecycle operations such as booking cancellation or confirmation retrieval. For a simulated flow, the core coverage is strong, with only minor gaps.
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