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Search Japan in Seasons

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Read-onlyIdempotent

Search live seasonal travel data for Japan cherry blossom forecasts, autumn leaves, festivals, and more. Retrieve result IDs for detailed guides.

Instructions

Use this for ChatGPT/deep-research style retrieval over Japan in Seasons. Searches live seasonal-travel dataset guides and returns result IDs for fetch. Use for questions about Japan cherry blossom forecasts, autumn leaves, seasonal flowers, festivals, fruit picking, weather, or the MCP server itself. Do not use for hotels, flights, trains, visas, or restaurants.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language search query, for example 'Japan cherry blossom forecast', 'Kyoto autumn leaves', or 'fruit picking in Japan in September'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYesThe tool's user-facing answer as Markdown or JSON text.
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint; description adds detail about live dataset retrieval and returning result IDs for fetch, providing useful behavioral context beyond annotations.

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 concise sentences front-load the purpose, include usage guidance, and no redundant 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?

With one parameter, clear description, comprehensive annotations, and knowledge of output schema, the description fully enables correct tool selection and invocation.

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

Parameters5/5

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

Only one parameter (query), and the description adds natural-language examples (e.g., 'cherry blossom forecast') that enhance the schema's already complete description.

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?

Clearly states the tool performs 'retrieval' over the 'Japan in Seasons' dataset, distinct from sibling tools. Lists specific topics it handles and excludes unrelated areas like hotels or flights.

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?

Provides explicit positive use cases (cherry blossoms, autumn leaves, etc.) and negative guidance (do not use for hotels, flights, etc.), leaving no ambiguity about when to choose this tool over siblings.

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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