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evaluate_store_location

Read-only

Evaluate store location suitability considering foot traffic, transport, competitor distribution. 10 prefectures. | 店舗出店適地評価。人流・交通・競合店分布を考慮したスコアを算出。全10都道府県。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYes市区町村(例: '名古屋市中村区')
radiusMNo競合・施設を検索する半径(メートル)
storeTypeYes出店を検討する店舗タイプ
prefectureNo都道府県名(和名/英名/ISO 3166-2 コード対応)愛知県
neighborhoodNo町丁目(例: '名駅南1丁目')。v2.4 では町丁目レベル実データに対応(対応都道府県のみ)
customWeightsNoカスタム重み付け(省略時はタイプ別デフォルト)
includeMarkdownNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already indicate read-only, non-destructive, and non-open-world behavior. The description adds the constraint of '10 prefectures' and the Japanese text mentions that a score is calculated. However, it does not disclose any other behavioral traits such as rate limits, side effects, or output format beyond the score mention.

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?

The description is extremely concise, two short sentences in English plus a Japanese translation, and the key purpose is front-loaded. Every word earns its place; there is no fluff or redundancy.

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

Completeness2/5

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

Given the tool's complexity (7 parameters, nested objects, no output schema), the description is too brief. It does not explain what the tool returns (beyond a vague 'score' in the Japanese text), does not list the 10 prefectures, and provides no caveats or prerequisites. An agent would not know the full scope or output format from this description alone.

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 86%, so the parameters are already well-documented in the schema. The tool description does not add any parameter-specific meaning beyond what the schema provides. With such high coverage, a baseline of 3 is appropriate, and the description neither enhances nor detracts.

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 states a clear verb ('Evaluate') and resource ('store location suitability'), listing specific considerations (foot traffic, transport, competitor distribution). It does not explicitly differentiate from sibling tools, but the subject matter is distinct enough that an agent can infer its use. The '10 prefectures' constraint adds scope.

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 purpose implies usage (evaluate a potential store location), but there is no explicit guidance on when to use this tool versus alternatives, nor any stated exclusions or prerequisites. The mention of '10 prefectures' hints at a coverage limit but does not clarify which ones or when to switch to another tool.

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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TDQS

B3.3/5.0
Disambiguation3/5

Many tools have overlapping purposes (e.g., analyze_renovation_yield vs recommend_renovation_targets, multiple scoring functions). While descriptions provide some differentiation, an agent could easily confuse tools like assess_property_risk, assess_family_friendly_score, and composite_value_score, all of which aggregate multiple axes into a single score.

Naming Consistency4/5

Most tools follow a verb_noun pattern (analyze_, assess_, get_, simulate_, etc.), but a few deviate with noun phrases (composite_value_score, portfolio_optimizer, scenario_what_if) or adjective-led names (quick_visual_summary). The pattern is largely consistent with minor exceptions, making it predictable overall.

Tool Count2/5

With 33 tools, the surface is quite heavy and exceeds the 25-tool threshold. While the server covers a broad domain (real estate analysis, simulation, contract review, reporting), many tools could be consolidated (e.g., multiple scoring functions). The count feels overwhelming for an agent to manage efficiently, though the scope is comprehensive.

Completeness4/5

The tool set covers the primary workflows of real estate intel: search/discovery, data retrieval, scoring, simulation, reporting, and contract support. Minor gaps exist (e.g., no direct property transaction listing lookup or lease-specific analysis), but these are not core to the server's stated purpose. The lifecycle of analysis is well-supported.