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sugukurukabe

japan-real-estate-intel

WhatIfシナリオ分析

scenario_what_if
Read-onlyIdempotent

Simulate the impact of new stations, commercial facilities, or population changes on land prices and investment scores across 10 prefectures in Japan.

Instructions

What-If scenario analysis: simulate impact of new stations, commercial facilities, population changes on land prices and investment scores. 10 prefectures. | シナリオWhat-If分析。新駅・大型商業施設・人口変動の地価影響を試算。全10都道府県。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYes市区町村(例: '名古屋市中村区')
scaleNo規模感。large=大型施設・急成長などmedium
horizonNo3y
scenarioYesシナリオ種別
prefectureNo都道府県名(和名/英名/ISO 3166-2 コード対応)愛知県
includeMarkdownNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYes
scaleYes
horizonYes
baselineYes現状ベースライン
keyRisksYes
scenarioYes
projectedYesシナリオ適用後予測
confidenceYes
prefectureYes
riskImpactPctYes
markdownReportNo
priceImpactPctYes地価への影響(%。正=上昇)
recommendationsYes
keyOpportunitiesYes
humanFlowImpactPctYes
Behavior2/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description only says 'simulate impact', adding no further behavioral context about rate limits, authentication needs, or any side effects beyond what annotations provide.

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?

Two sentences, bilingual, front-loaded with key action and scope. Every word adds value; no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential function and scope, and an output schema exists to explain return values. However, it lacks details on limitations (which 10 prefectures, how results are tabulated) and could be more complete for a complex simulation tool.

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?

With 67% schema coverage, the schema already documents most parameters adequately. The description adds no extra meaning beyond summarizing the tool's overall function. A score of 3 is appropriate as baseline.

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 simulates impact of various scenarios on land prices and investment scores, with a specific scope of 10 prefectures. It effectively distinguishes from sibling tools like forecast_land_price_trend or simulate_landscape_impact by focusing on what-if analysis.

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

Usage Guidelines3/5

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

The description implies the tool is for exploratory scenario analysis but does not provide explicit guidance on when to use it versus alternatives. No when-not-to-use conditions or comparisons to siblings are given.

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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