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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds non-obvious behavioral context: support percentages are calculated by Velarion from disclosed vote counts and marked as such in the citation. This is valuable beyond the structured annotations, with no contradiction.

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 short and front-loaded, and the methodology note earns its place. The first sentence largely restates the title, creating minor redundancy, but overall there is no wasted content.

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 simple read-only nature, full parameter documentation, and presence of an output schema, the description covers the key caveat about calculated percentages. It is complete enough for correct invocation, though usage guidance is absent.

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?

Input schema coverage is 100%, with both ticker and agent_token already described. The description does not add parameter-level detail, so baseline 3 is appropriate since the schema carries the full burden.

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 identifies the resource as say-on-pay vote history with a specific verb ('get' implied). It is distinguishable from siblings like predict_say_on_pay_risk by the vote-history scope, though it does not explicitly contrast itself with any sibling.

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?

No guidance is given about when to use this tool versus alternatives such as get_company_compensation, get_ceo_pay_ratio, or predict_say_on_pay_risk. The 'Free plan' label hints at access conditions but does not explain prerequisites or exclusions.

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