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

No annotations are provided, so the description carries the full behavioral burden. It does disclose data provenance (OpenPOI public-facility category aggregation), which is useful beyond structured fields, but says nothing about whether values are absolute counts or normalized, the time period / snapshot of the data, whether all 47 prefectures are covered, or the response shape for an unknown prefecture.

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?

It is a single short phrase with no padding, so nothing is wasteful. However, it is under-specified rather than concise: there is no verb, no clause structure, and no front-loaded statement of what the caller gets back.

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?

With no annotations, no output schema, and an undocumented required parameter, the description should carry far more than one noun phrase. An agent cannot confidently construct the call or interpret the result, and the description is written only in Japanese, adding a comprehension barrier for non-Japanese-speaking agents.

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 0% for the single required parameter 'prefecture', and the description never mentions it. The agent is left guessing whether to pass '東京都', 'Tokyo', '13', or a romaji slug — a critical ambiguity for a lookup keyed on prefecture that will silently produce wrong or empty results.

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 names a concrete metric and source: a prefecture-level public-facility count signal derived from OpenPOI category POI aggregation. That is more than a restatement of the name 'jp_facility_signal', but it is a bare noun phrase with no verb and no indication of what the agent receives (raw counts, normalized index, ranking). It does not distinguish itself from structurally similar siblings such as jp_tourism_signal or jp_density beyond the metric name.

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 when-to-use guidance, no statement of the question this signal answers, and no named alternative among the 15 siblings. The agent must infer that this is a prefecture-level demographic/infrastructure lookup purely from the word 都道府県別.

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