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How far ahead to book this route

when_to_book

How many days before departure this route has historically been cheapest, from our own observations. Answers "am I booking too early or too late?".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originYesOrigin CITY NAME, e.g. "Milan". Airport codes are not accepted.
destinationYesDestination CITY NAME, e.g. "Tokyo". Airport codes are not accepted.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / destination / description
      Previous value: -"Destination city or IATA code, e.g. \"Tokyo\" or \"NRT\"."New value: +"Destination CITY NAME, e.g. \"Tokyo\". Airport codes are not accepted."
    • changedInput schema / properties / origin / description
      Previous value: -"Origin city or IATA code, e.g. \"Milan\" or \"MXP\"."New value: +"Origin CITY NAME, e.g. \"Milan\". Airport codes are not accepted."
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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