Skip to main content
Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PYTHONPATHNoSet to /app/src for proper module resolution/app/src
MCP_LOG_LEVELNoSet logging level (DEBUG, INFO, WARNING, ERROR)INFO
PYTHONUNBUFFEREDNoSet to 1 for real-time logging1

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_now_showingA

Returns a list of movies currently showing in a given city or ZIP code.

get_recommendationsA

Suggests movies based on mood, genre, or time preferences.

get_showtimesB

Fetches available showtimes for a specific movie and location.

get_seat_mapA

Displays available and reserved seats for a specific showtime.

book_seatsC

Reserves selected seats for the user.

process_paymentC

Handles simulated payment transaction.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A3.6/5.0

Scored across 6 tools

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.