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

Annotations already cover the non-destructive, read-only, idempotent behavior, so the bar for added disclosure is lower. The description adds meaningful context about the content returned (resolutions, votes, pass/fail, support percentages) and the NZX scope, without contradicting the annotations.

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 short sentences with no filler or redundancy: the first states what the tool returns, and the second gives the use case. The key scoping element is front-loaded.

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 simple parameters, an output schema, and annotations that establish read-only/idempotent behavior, the description covers everything needed to invoke the tool correctly and interpret its purpose. There is no obvious missing context for this scope.

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%, so the schema already documents ticker and year. The description adds no meaning beyond 'voting data for an NZX company,' which only reinforces the obvious ticker parameter, so it stays at the baseline 3.

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 names a specific verb ('Get'), a specific resource ('AGM resolution voting data'), and a scope ('for an NZX company'). It also lists the concrete data elements (votes for/against/abstain, pass/fail, support percentages), which distinguishes it from related governance siblings like get_governance_scores and get_board_changes.

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 gives an explicit intended use: 'Use for governance and shareholder activism analysis.' It does not name exclusion conditions or sibling alternatives, so an agent must infer when another tool is more appropriate, but the stated context is clear enough for a first selection.

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