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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

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
}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_metarA

Current METAR observations for one or more airports, normalized into a structured schema. Use this when you need the raw current conditions. If you need a judgment about whether conditions are flyable, use assess_conditions instead — it will tell you when it is not sure.

get_tafA

Terminal aerodrome forecasts (TAFs) for one or more airports. Forecasts, not observations — use get_metar for what the weather is doing right now.

search_stationsA

Find weather-reporting stations by name or partial ICAO identifier. Use this to resolve a place name to an ICAO id before calling the other tools.

assess_conditionsA

Assess current flight conditions at an airport. Returns a flight category (VFR/MVFR/IFR/LIFR), a confidence score, the specific observations behind the call, and a requiresHumanReview flag. IMPORTANT: when requiresHumanReview is true, do not present the category as settled — the conditions are near a category boundary, the report is missing data, significant weather is present, or sources disagree. Surface the reviewReasons and hand off to a person.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.2/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: search_stations resolves identifiers, get_metar fetches raw observations, get_taf fetches forecasts, and assess_conditions provides a judgment. The descriptions explicitly differentiate get_metar vs get_taf and assess_conditions, eliminating overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (search_stations, get_metar, get_taf, assess_conditions) with snake_case throughout. No deviations or mixed conventions.

Tool Count5/5

With 4 tools, the set is minimal but perfectly scoped for aviation weather needs. Each tool has a clear, non-redundant role, and the count avoids bloat.

Completeness4/5

The core aviation weather workflow (resolve station, get current/forecast weather, assess conditions) is fully covered. However, the absence of tools for historical weather, PIREPs, or other advisories leaves minor gaps that an agent might need to work around.

Maintenance

ActivityStale
ResponsivenessNo issues