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

Annotations already declare readOnly, idempotent, non-destructive and non-open-world, so the safety profile is covered. The description adds genuinely new behavioral context the annotations cannot convey: the result set is a processed subset with incomplete market coverage and non-current personnel. It stops short of describing pagination, result ordering, or freshness, which keeps it below a 5.

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, front-loaded with the lookup action and followed by the coverage caveat; nothing is filler. The only cost is that the scope caveat is slightly detached from the main clause and the phrasing is a touch indirect.

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?

With an output schema present, return-shape explanation is unnecessary, and the coverage caveat covers the main data-quality risk. What is missing is positioning relative to the eight siblings, especially get_leadership, which an agent would otherwise likely confuse with this list tool.

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% for the single 'query' parameter, so the description must compensate. It does clarify the accepted identifier forms (name, verified ticker, CIK), which is the key semantic, but it says nothing about matching behavior (partial vs exact), the empty-string default, or what an omitted query returns. That is only partial compensation for the coverage gap.

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 concrete verb and resource ('Find saved executive/board disclosures') plus the search keys (name, verified ticker or CIK), so the agent knows it is a lookup over a curated leadership dataset. It does not name any sibling, and the noun 'disclosures' sits slightly awkwardly against the tool name 'list_leadership_companies', leaving mild ambiguity about whether it lists companies or filings.

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 second sentence gives useful negative scoping ('not all-market coverage or a current officer roster'), which implicitly tells the agent when not to rely on it. However, there is no positive routing guidance against the obvious alternatives get_leadership, get_leadership_passage or list_earnings_companies, so the agent must infer when this list-level tool is preferable to the per-company getters.

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