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

Annotations already declare readOnly/idempotent/non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral context beyond that: the output is a probabilistic 9-factor score bounded 0-95% and the inputs it is derived from. It does not discuss latency, caching or score interpretation thresholds.

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?

Front-loaded with the purpose and score range, then the factor list, then the usage condition. The nine-factor enumeration is dense but earns its place by explaining what the score represents. Slightly long, but no sentence 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?

An output schema exists so return values need not be described; the description still supplies the score range and composition, which is the key interpretive context. For a single-parameter read tool with full annotations and an output schema, this is near-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 100% and there is only one parameter (ticker), which the schema already documents with examples. The description adds nothing about the ticker format or constraints, so the baseline 3 applies.

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?

States a specific verb and resource ('Get dividend cut probability for an NZX company') and immediately narrows scope with the model definition (9-factor, 0-95%). This distinguishes it clearly from adjacent siblings like get_dividend_stress_test and get_dividends without the agent opening any schema.

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?

'Use when evaluating dividend sustainability or screening for at-risk dividends' gives a concrete triggering context for the tool. It stops short of naming alternatives (e.g. get_dividend_stress_test) or stating when not to use it, so it is clear context without explicit routing.

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