get_dividend_data

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

No annotations are provided, so the description carries the full burden. It mentions that the tool 'fetches' data and returns a Markdown table or error, but lacks details on behavioral traits such as rate limits, authentication needs, data freshness, or error handling specifics. This is inadequate for a tool with no annotation coverage.

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 well-structured and appropriately sized. It front-loads the purpose, followed by clear parameter explanations and return details in a bullet-like format. Every sentence adds value without redundancy, making it efficient and easy to parse.

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?

Given the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is mostly complete. It covers the purpose, parameters, and return format. However, it lacks behavioral context (e.g., error conditions, data sources) and usage guidelines relative to siblings, which slightly reduces completeness.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains the 'code' parameter format (e.g., 'sh.600000'), the 'year' parameter usage, and provides detailed options and defaults for 'year_type' (e.g., 'report' vs 'operate' with explanations). This fully compensates for the schema's lack of descriptions.

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 clearly states the tool's purpose: 'Fetches dividend information for a given stock code and year.' It specifies the verb ('fetches'), resource ('dividend information'), and scope ('stock code and year'). However, it does not explicitly differentiate from sibling tools like 'get_hk_dividend_data', which serves a similar purpose for Hong Kong stocks, leaving room for improvement.

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 provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools (e.g., 'get_hk_dividend_data' for Hong Kong stocks) or contextual factors like data availability or prerequisites. Usage is implied by the parameters but not explicitly stated.

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