get_wifi_factsBest answer for "will this flight have WiFi". Predicts the WiFi provider for a flight number on a given date, with probability, the tail number expected to operate it, aircraft type, and the airline fleet fallback. Prefer this over get_flight_wifi.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | Flight date in YYYY-MM-DD format. Defaults to today. | |
| flight_number | Yes | IATA flight number, e.g. "UA123" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds value by detailing the expected output components (provider probability, tail number, aircraft type, fleet fallback). It doesn't contradict annotations and provides useful context about the tool's predictive behavior.
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?
Two sentences deliver purpose, output scope, and tool preference with minimal verbosity. Front-loaded with 'Best answer', it is efficient and well-structured.
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?
For a read-only prediction tool with two parameters and no output schema, the description lists key output fields and provides selection guidance. The term 'airline fleet fallback' could use elaboration, but overall it's adequate for invoking 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?
Schema coverage is 100% for both parameters, so the description adds little beyond what the schema provides. It mentions flight number and date but provides no additional format or semantics beyond schema descriptions.
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 tool's purpose: predicting WiFi provider for a flight number on a given date, including probability, tail number, aircraft type, and fallback. It uniquely frames itself as the 'best answer' for in-flight WiFi queries and distinguishes from sibling get_flight_wifi.
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 explicitly instructs to prefer this tool over get_flight_wifi, providing a direct alternative comparison. It also implies the primary use case ('will this flight have WiFi'), giving clear contextual guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but get_flight_wifi and get_wifi_facts both answer flight-level WiFi questions, creating overlap. The descriptions help by directing users to get_wifi_facts as the preferred choice, yet the redundancy remains. Other tools like get_airline_wifi vs list_airlines are differentiated by scope, but the core flight lookup is duplicated.
All tool names follow a consistent verb_noun pattern in snake_case: get_airline_wifi, get_flight_wifi, get_rollouts, get_speed_stats, get_tail_info, get_wifi_facts, list_airlines, search_wifi. The verbs (get, list, search) are appropriate and uniform. No mixed naming conventions or camelCase deviations are present.
The 8 tools are well-scoped for a WiFi information server, covering airline, flight, aircraft, speed, rollout, and search capabilities without overwhelming redundancy. Each tool serves a distinct need (except the noted overlap), and the count sits comfortably in the ideal range.
The tool set comprehensively covers the in-flight WiFi domain: airline-wide info, flight-specific predictions, tail history, speed statistics, rollout progress, and free-text search. No critical operations are missing, and the inclusion of both airline and flight level lookups with fallback logic makes the surface complete for read-only information retrieval.