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

With no annotations present, the description carries the full behavioral burden and does disclose two meaningful traits: the result set is a complete fixed enumeration of 22 administrative units, and cities with no institutions are explicitly returned as 0 rather than omitted. It omits ordering, but for a zero-parameter read of a small fixed dataset that gap is minor.

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?

A single sentence with no filler, front-loading the scope (22 縣市) and the key caveat (zero-filled for empty units). Nothing is wasted.

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?

There is no output schema, so the description has to describe the return values, and it does: 22 city/county entries plus counts with explicit zero-filling. Only ordering and the exact field format remain unspecified, which is minor for a small fixed payload.

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?

The tool takes zero parameters, so the baseline is 4 and there is no parameter meaning left to add; the description correctly allocates its words to the return shape instead.

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 uses a specific verb (回傳) paired with a precisely scoped resource (the 22 縣市 and their 機構數), so an agent knows exactly what it will get back. It does not distinguish itself from the sibling list_district_counts, which offers an apparently similar aggregation at a different granularity, leaving that differentiation to the caller's inference.

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 prerequisites stated, and no mention of the obviously related list_district_counts alternative. The agent must guess whether city-level or district-level counts are wanted.

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