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Search Japan in Seasons

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Read-onlyIdempotent

Search live seasonal travel data for Japan including cherry blossom forecasts, autumn leaves, fruit picking, flowers, festivals, and weather. Enter a natural-language query to retrieve relevant result IDs for detailed information.

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'.
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds that the tool 'returns result IDs for fetch', clarifying output format and highlighting it as a search step, which is not evident from annotations alone. It also specifies 'ChatGPT/deep-research style retrieval' indicating semantic search behavior.

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 sentences, each purposeful: first states purpose and style, second specifies data source and output, third gives usage guidelines. No redundant language; concise and 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?

Despite having no output schema and only one parameter, the description fully explains the tool's role among many siblings, its input, output format, and usage boundaries. It is complete given the tool's complexity.

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?

The single parameter 'query' is already well-described in the schema with examples. The description adds value by explaining the data source ('live seasonal-travel dataset guides') and output ('returns result IDs for fetch'), providing context beyond the schema. Schema coverage is 100%, baseline is 3, and the extra context raises it.

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 verb 'searches', identifies the resource as 'live seasonal-travel dataset guides', and distinguishes it from siblings by positioning it as a general search tool for a broad domain, while siblings are more specific. The title 'Search Japan in Seasons' reinforces the resource.

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?

Explicitly provides when to use (e.g., cherry blossoms, autumn leaves, festivals) and when not to use (e.g., hotels, flights, restaurants), offering clear exclusions and context for appropriate invocation.

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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