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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description's 'predict/算出' language is consistent with a read operation, so no contradiction exists. The description does add the useful '10 prefectures / 全10都道府県' scope constraint. However, it discloses nothing beyond that — no mention of input data sources, what the scores mean, output shape, or that only a subset of prefectures have full data (which the neighborhood schema description hints at). With read-only safety already covered by annotations, a 3 is fair: acceptable but light on behavioral detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

At two short sentences with the core action front-loaded, it's tightly written and scannable. Half a point is lost to redundancy: the Japanese sentence is a near-verbatim translation of the English one (企業立地需要予測 ≈ 'predict corporate demand'), so roughly half the description repeats information already provided rather than adding distinct value for the consuming agent.

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

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

The tool has 5 parameters, including an enum, and — critically — has no output schema, yet the description never explains what the call returns: score ranges, format, units, or how scores map to the three demand categories. For a scoring/prediction tool whose return contract is undocumented, this is a real gap. The geographic scope (10 prefectures) is there, but an agent cannot reason about the result without reading more; given the complexity and missing output schema, the description is noticeably incomplete.

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

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

Schema description coverage is 80%, so the schema already documents most parameters (prefecture name formats, neighborhood example, propertyType enum, commute boolean). The description adds one genuinely useful semantic clue — the '10 prefectures' constraint on the valid geographic scope — but says nothing about the area/prefecture/neighborhood hierarchy or how includeCommuteAnalysis changes the result. The schema does the heavy lifting, so the description only marginally raises the baseline of 3.

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

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

The description states a specific verb ('Predict') and resource ('corporate demand') plus the three demand categories (manufacturing, office, retail) and scope (10 prefectures), which separates it from siblings like evaluate_store_location by telling the agent what domain it covers. It loses a point because 'corporate demand scores' is not operationalized — it never says what these scores represent or how they're expressed, and the bilingual repetition of the same two sentences adds no new meaning.

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

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

There is no guidance on when to use this tool versus alternatives. Siblings such as evaluate_store_location, get_population_outlook, compare_prefectures, and discover_opportunities plausibly overlap with corporate-demand prediction, yet the description offers no selection criteria, exclusions, or 'use X instead' routing. The agent is left to infer relevance from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.