get_fins_dividend

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

Annotations already declare read-only, idempotent, and non-destructive behavior, lowering the burden. The description adds meaningful context beyond annotations: it lists the specific dividend fields returned, the Premium plan requirement, and a nuance about 4-digit codes matching ordinary shares only. It does not mention rate limits or error behavior, but the provided additions are valuable and consistent.

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?

The description is tightly written with no redundant phrases. It opens with a clear one-sentence purpose, follows with a compact list of return fields, a short plan note, and a structured Args block. Every sentence adds value, and the layout is easy to scan.

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?

An output schema exists, so return format is covered. However, the description does not clarify parameter relationships or requirements: all parameters are optional in the schema, but it is unclear whether a code and a date/range are expected, or if date excludes date_from/date_to. This ambiguity could lead an agent to invoke the tool with an invalid parameter combination. Given the tool complexity, a few more usage constraints would make it complete.

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%, so the description must fully compensate for parameter semantics. It does: code is explained with an example and a note about ordinary shares; date format is specified; date_from and date_to are clearly labeled as range query boundaries. Each parameter gains meaning beyond the bare schema, making this parameter documentation complete and self-sufficient.

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 starts with a clear verb and resource: 'Retrieve cash dividend data.' It then lists the specific data fields returned (record date, ex-dividend date, dividend amount, expected payment start date, and commemorative/special dividends), which distinguishes it from other tools like get_dividend_yield_ranking or detect_consecutive_dividend_increase. The purpose is unambiguous and specific.

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?

The description implies when to use this tool (when you need cash dividend data for a stock) and provides constraints like '4-digit codes match ordinary shares only' and '[Supported plans] Premium'. However, it does not explicitly contrast this tool with alternatives or state when NOT to use it, such as pointing to get_dividend_yield_ranking for yields. The usage context is clear but lacks exclusionary guidance.

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