Get a buy/wait/neutral recommendation for a flight fare based on historical patterns and time-based signals.
| Name | Required | Description | Default |
|---|---|---|---|
| origin | Yes | IATA airport code for departure (e.g. JFK) | |
| destination | Yes | IATA airport code for arrival (e.g. LAX) | |
| currentPrice | Yes | Current fare price in USD that you are considering | |
| departureDate | Yes | Departure date in YYYY-MM-DD format |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds behavioral context by specifying that the output is a buy/wait/neutral recommendation based on historical and time-based signals. No contradiction with annotations exists.
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, focused sentence that front-loads the outcome and includes the core basis for the recommendation. There is no filler, redundancy, or unnecessary restatement of schema details.
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 fully described parameters and annotations, the description is nearly complete: it names the output categories and the basis for the recommendation. It lacks explicit guidance on interpretation or sibling routing, but the schema and annotations cover the operational essentials.
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%, and all four parameters already have clear descriptions and constraints in the schema. The tool description adds no additional parameter-level meaning, 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 uses a specific verb ('get') and names an exact resource: a buy/wait/neutral recommendation for a flight fare. It distinguishes itself from sibling search/list/optimization tools by emphasizing historical patterns and time-based signals.
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 the tool is used when a fare recommendation is needed, but it does not explicitly state when to choose it over search_flights, list_deals, or optimize_points. No alternatives are named and no exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct decision point—searching routes, browsing deals, finding flexible dates, predicting fares, and comparing points value. Search_calendar and search_flights are similar, but descriptions clarify date-flexible versus specific-date/route search.
All tool names follow a consistent lowercase snake_case verb_noun pattern: get_prediction, list_deals, optimize_points, search_calendar, search_flights. There is no mixing of conventions or vague verbs.
Five tools is a well-scoped size for a travel deals and points optimization assistant. Each tool earns its place and none feel redundant or unnecessary.
The set covers the core travel decision workflow: searching flights, viewing flexible dates, browsing deals, predicting fare movements, and optimizing points redemption. A fare alert or booking tool is absent, but those are beyond the apparent decision-support scope.
