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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), and the description adds the data provenance and the digit-dependent response mode, which is real behavioral context. It stops short of stating units, period/currency, or how many subsectors a 2-3 digit query returns.

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 sentences, front-loaded with what it returns, followed by the input-mode rule. The metric list is dense but earns its place since there is no output schema to enumerate return fields.

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 no output schema, the description does the work of telling the agent what fields come back, and it covers the single parameter's behavior. Missing details on units, reference period, and result size keep it from being fully complete for a benchmarking 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 coverage is 100%, so the baseline would be 3, but the description adds semantics the schema does not: digit count changes the response mode (benchmarks vs. subsector listing). Minor tension with the schema's '2-6 digits; matched on its first 4 digits' wording, but not contradictory.

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+resource (industry benchmark ratios by NAICS subsector), names the data source (Damodaran / NYU Stern as used by Equitest), and enumerates the exact metrics returned. This is clearly a sector-level lookup, distinguishable from company-level siblings like get_company_financials and get_company_valuation.

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?

Explicitly tells the agent how the input depth selects behavior: a 4-digit code returns benchmarks, 2-3 digits lists the subsectors underneath. It gives clear context but never states when to prefer this over siblings (e.g., to contextualize a company's ratios) or any exclusions.

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