get_last_minute_dealsCurrent last-minute hotel deals in a city: live rates below each hotel's tracked typical price, plus hotels that historically drop prices close to check-in.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name, e.g. 'Lisbon' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description bears full burden. It reveals that deals include 'live rates below each hotel's tracked typical price' and hotels that 'historically drop prices', which is useful behavioral context. However, it does not mention data freshness, caching, or any side effects, leaving gaps in transparency.
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 concise with two sentences that front-load the main purpose. Each sentence adds value: the first defines the output, the second clarifies the deal comparison. Minor structural improvement could include bullet points for clarity, but overall efficient.
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 single parameter with full schema coverage and no output schema, the description should elaborate on the response format. It mentions 'live rates' and 'typical price' but not whether the output is a list of hotels, price reductions, or percentages. This gap reduces completeness for an agent to use the tool effectively.
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% with the 'city' parameter described as 'City name, e.g. 'Lisbon''. The description only restates that deals are 'in a city', adding no new meaning beyond the schema. Baseline 3 is appropriate as the parameter is simple and well-documented.
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 it provides 'current last-minute hotel deals in a city' and specifies the comparison to 'tracked typical price' and historical patterns. This uniquely identifies the tool's purpose and distinguishes it from siblings like 'get_booking_advice' which offers booking recommendations, and 'get_city_calendar' which shows events.
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 when to use (to find last-minute deals) but does not explicitly state when not to use it or mention alternatives. It lacks guidance on prerequisites or comparison with siblings, relying 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.
Add one secure layer between your agents and this server.
Each tool addresses a distinct aspect: booking advice, calendar events, last-minute deals, and city listing. No functional overlap.
All tool names follow a consistent verb_noun snake_case pattern (get_*, list_*), making them predictable.
With 4 tools, the server is compact yet covers the core functionalities of a hotel booking-timing service.
The tool set provides all necessary operations for the declared domain: advice, calendar, deals, and city discovery. No obvious gaps.