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Fruit Picking Farms

fruit_farms
Read-onlyIdempotent

Find fruit-picking farms in Japan with booking links and map coordinates. Filter by month, fruit, or region to get relevant results.

Instructions

Use this when the user needs actual fruit-picking farms, booking links, and map coordinates. Returns farms from the local dataset, and month filtering automatically narrows results to fruits that are in season. If the user only asks which fruit is in season, call fruit_seasons first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthNoOptional travel month from 1 to 12. Filters to farms with at least one fruit in season during that month, for example 5 for May strawberry farms.
fruitNoOptional fruit name such as 'Strawberry', 'Apple', 'Grape', 'Peach', 'Cherry', or 'Mikan'. Matching is case-insensitive. Use with or instead of month.
regionNoOptional prefecture, city, or region substring such as 'Yamanashi', 'Nagano', 'Aomori', or 'Tokyo'. Partial case-insensitive matching is supported against farm names and addresses.
limitNoOptional maximum number of farms to return. Default is 30 and the hard maximum is 100.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYesThe tool's user-facing answer as Markdown or JSON text.
Behavior4/5

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

Annotations already indicate readOnly and idempotent. The description adds that month filtering automatically narrows to in-season fruits, which is a behavioral detail beyond annotations. No contradictions.

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?

Three front-loaded sentences: first states use case, second describes behavior, third gives alternative. No irrelevant information; every sentence serves a purpose.

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?

Given the tool has 4 optional parameters, read-only behavior, and an output schema, the description covers essential context: what it returns, filtering behavior, and when to use alternatives. Complete for effective agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (baseline 3). The description adds value by explaining that month filtering narrows results automatically and mentions case-insensitive matching, enhancing understanding beyond schema.

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 clearly states the tool provides fruit-picking farms, booking links, and map coordinates. It distinguishes from sibling fruit_seasons by specifying when to use that alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool (when user needs actual farms) and when not to (if only season info, call fruit_seasons first), providing clear alternative 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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