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

With no annotations, the description carries the full burden. It discloses the return fields, notes no LLM involvement, and mentions the CEO-only limitation. It does not address idempotency, security, or rate limits, but covers core behavioral aspects adequately.

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 extremely concise—two sentences plus a short note—with no wasted words. It is front-loaded with the primary purpose and details are separated clearly.

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 has 4 parameters (0% coverage) and no annotations, the description is moderately complete. It lists return values and a key limitation (CEO-only) but lacks parameter descriptions, usage examples, or error handling. An output schema exists, which reduces some burden.

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%, so the description must compensate. It only adds context for the role parameter (CEO-only), but provides no explanation for ticker, fiscal_year, or agent_token. This is insufficient for the agent to understand parameter usage.

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 benchmarks executive pay vs disclosed peers using Velarion's canonical percentile data, listing specific return metrics. It distinguishes itself from sibling tools like lookup_company_compensation and predict_say_on_pay_risk by focusing on percentile-based benchmarking.

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 notes that role is currently CEO-only, providing a key constraint, but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. Usage context is implied but not fully guided.

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