Piratefly Flight Price Judge

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the disclosure burden and does a solid job: it reveals the tool uses observed median prices, produces a ranked month list, and avoids speculative folklore. It does not mention data period, currency, or error behavior, but the core behavior is transparent for a simple read-only tool.

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 concise sentences that front-load the ranking behavior and the median output. Every sentence adds value, with no redundant or filler language.

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?

Despite having no output schema, the description conveys the return shape: ranked departure months with each month's median price. Combined with fully documented schema parameters, this is sufficient for an agent to call the tool correctly, though minor details like the historical window or currency are not stated.

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 both required parameters already have clear descriptions including the city-name requirement and airport-code rejection. The tool description adds no parameter-level detail, 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 clearly states the action and resource: departure months ranked by observed median price for a route, with the median of each. It answers a concrete question ('when is it cheapest to go') and explicitly distinguishes itself from folklore-based guessing.

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 gives a clear use context: answering when it is cheapest to travel based on measured data. It implies this tool is for month-selection rather than booking timing or per-fare verdicts, but it does not explicitly name when to prefer it over the sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden and does well by disclosing the underlying data window (90 days), the nature of the operation (judging a price, not modifying anything), and the specific outputs (verdict, percentile, median, all-time low, sample size). It does not mention error cases or data availability limits, but the core behavior is transparent.

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 three concise sentences, with the core answer front-loaded. The second sentence prevents misuse by excluding flight search, and the third explains the unique data requirement. No repetition or filler; every sentence contributes useful context.

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 two-parameter tool with no output schema, the description effectively covers the return values and the tool's non-search nature. It does not elaborate on edge cases or how the percentile should be interpreted, but the essentials—how to call, what to expect, and when it applies—are present. Sibling differentiation could be stronger, but it is adequate.

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 both origin and destination thoroughly described including format constraints and examples. The description adds no new parameter-level detail beyond calling it a 'route,' so the baseline score of 3 applies.

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 states a specific purpose: answering 'buy now or wait?' for a route based on 90 days of price history. It clearly distinguishes itself from flight search by saying 'This is not a flight search: it judges a price.' This separates it from any search-like siblings and defines the tool's resource as price verdicts.

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 phrase 'buy now or wait?' clearly signals when to use the tool: when deciding whether to purchase a fare for a known route. It explicitly excludes flight search scenarios, but does not name or contrast the sibling tools cheapest_months and when_to_book, so alternative selection is left somewhat implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It explains the list semantics: routes observed daily, "therefore judge," and filter behavior including partial city matching and full list behavior. It does not describe return format or error cases, but it is transparent about selection behavior and read-only listing nature.

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?

Three tight sentences with zero wasted wording. The core purpose is front-loaded, usage comes next, and filter behavior closes with an explicit full-list option. Every sentence earns its place.

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 simple two-optional-parameter listing tool with no output schema, the description provides enough to call it correctly: what the list represents, when to call it, and how to filter. An agent can select and invoke it confidently.

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% and both optional parameters already document the contains-text filtering semantics. The description adds value by stating "partial city name is fine" and "omit both for the full list," which clarifies match behavior beyond the bare schema.

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?

Uses a specific verb and resource: "Lists the routes Piratefly observes daily and can therefore judge." This identifies the resource (watched routes) and explicitly ties it to the judgment capability that distinguishes it from pricing/timing siblings like flight_price_verdict, cheapest_months, and when_to_book.

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?

Gives explicit usage guidance: "Call this first, or after any answer saying a route is not watched." It clearly states when in a workflow to invoke the tool, though it does not explicitly enumerate exclusions or contrast sibling tools by name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral burden. It is transparent about the data source ('from our own observations') and that the result is historical rather than guaranteed, but it does not explicitly state that it is a read-only query or describe any side effects. For a simple lookup tool this is adequate but not rich.

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 definition is a compact phrase plus a question that makes the purpose immediately understandable. There are no redundant sentences or restatements of the tool name, and the core 'days before departure' concept is front-loaded.

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?

The tool is simple: two required city-name parameters, no output schema, and no annotations. The description plus schema fully equip an agent to call it correctly and interpret the result as a historical day-offset. The sibling context clarifies the type of answer without needing a documented return type.

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?

The description itself adds little beyond the parameter names, but schema coverage is 100%: both origin and destination have detailed descriptions with examples and an explicit rejection of airport codes. The schema carries the parameter burden adequately.

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 directly states the tool's function: reports how many days before departure a route has historically been cheapest, framed as answering 'am I booking too early or too late?' This clearly distinguishes it from siblings cheapest_months (month-level timing) and flight_price_verdict (price assessment), so an agent can identify the right tool.

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 phrasing 'how many days before departure' and the question 'am I booking too early or too late?' provide clear context for when to invoke this tool. It does not explicitly name alternatives or state when not to use it, so it stops short of full exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.