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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly discloses what the tool returns: category counts, field counts, row counts, year ranges, and representative datasets. As a read-only catalog retrieval tool with no parameters, this level of transparency is sufficient.

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?

Two compact sentences with no filler. The core purpose is stated first, and the useful example question is included at the end. Every clause adds value.

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?

For a parameterless catalog-summary tool with no output schema, the description fully covers what the tool does, what data it returns, and when to use it. Nothing critical is missing for an agent to select and invoke it correctly.

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 has zero parameters, so parameter semantics are trivially satisfied. The description appropriately focuses on the output rather than inputs, which is exactly what an agent needs for a no-argument tool.

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?

Description states a specific verb+resource: retrieves a catalog summary of the FinLab quantitative database. It clearly specifies coverage (TW/US stocks, category counts, field counts, row counts, year ranges, representative datasets) and even names the target question it answers, distinguishing it from sibling tools like get_finlab_docs.

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?

Directly states the intended use case: '適合回答「FinLab 有哪些資料」' (suitable for answering 'what data does FinLab have'). This is explicit enough to guide selection, though it does not mention exclusions or contrast with siblings.

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