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sugukurukabe

japan-real-estate-intel

assess_exterior_visuals

Read-only

Assess building exterior, road width, and environment via Street View and AI vision. Falls back to simulated audit when imagery is unavailable.

Instructions

AI visual exterior audit of a property based on Google Street View static imagery + Gemini Vision AI. Falls back to simulated audit if API keys are missing. | AI街頭外観監査。ストリートビュー画像とGemini Vision AIを用いて建物の外観・道路幅・環境を自動評価。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNo市区町村(例: '名古屋市中村区'、'世田谷区')
pitchNoカメラの上下角 (-90=真下、0=水平、90=真上)
addressNo詳細な住所または建物名(例: '名駅南1丁目3-9')
headingNoカメラの向き (0=北、90=東、180=南、270=西)
latitudeNo緯度
longitudeNo経度
prefectureNo都道府県名(和名/英名/ISO 3166-2 コード対応)愛知県
Behavior4/5

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

Annotations declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it uses Google Street View static imagery + Gemini Vision AI, and falls back to simulated audit if API keys are missing. This supplements the annotations by explaining the technology stack and failure mode.

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 concise English sentences plus a Japanese translation. No fluff. Front-loads the core purpose immediately. Every sentence adds value.

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

Completeness3/5

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

The description covers the main function and technology, but lacks details about output format, required inputs (all optional may be confusing), and how to specify a property (address vs coordinates). With no output schema and many sibling tools, more guidance on expected results would improve completeness.

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?

Schema description coverage is 100%, so the input schema already documents all 7 parameters. The description does not add any additional meaning beyond what the schema provides (e.g., it doesn't clarify how pitch/heading relate to Street View camera control). Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs an AI visual exterior audit using Street View imagery and Gemini Vision AI. It names the specific resource (property exterior) and action (audit). However, it does not explicitly distinguish from sibling tools like 'quick_visual_summary' or 'simulate_landscape_impact', which may overlap.

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

Usage Guidelines2/5

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

No guidance on when to use this tool vs alternatives. It mentions a fallback if API keys are missing but does not indicate prerequisites (e.g., network access for Street View), typical use cases, or when not to use it. For a tool with many siblings, this is a significant gap.

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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