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

Annotations already mark this as read-only and non-destructive, but the description adds deep behavioral context beyond that: TTM bridge construction, P/E basis, enterprise value components, the rule that missing inputs are never estimated, and the guarantee that date and cohort basis are named in the response. This is exactly the kind of methodology disclosure that helps an agent trust and interpret the result.

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 long, but nearly every sentence adds a distinct, decision-relevant fact about methodology, scope, or routing to a sibling. It front-loads the core metrics and then layers important caveats. It could be mildly tightened with bullet-like separation, but it remains informative without fluff.

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 explaining what the response will contain: valuation multiples, peer statistics, REIT-specific multiples, and the guarantee that dates and cohort basis are named. It also gives the key routing instruction and all material data-quality constraints. For a one-parameter read-only tool, this is complete enough for an agent to invoke correctly and interpret the response.

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?

The schema already covers the only parameter ticker at 100% coverage, so the description is not required to add param-level detail. The description's methodology notes about reconciliation and USD are about the underlying data, not the ticker parameter itself. Baseline 3 is appropriate because the schema fully documents the parameter and the description does not meaningfully expand on it.

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 opens with a precise statement of what is returned: 'current EV/Revenue, EV/EBIT and P/E with peer median, quartiles and sample size.' It names distinct metrics and differentiates itself by explicitly pointing to GetValuationMultiplesHistory for history, so an agent can distinguish this from the closest sibling without guessing.

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 clearly states when to use this tool versus an alternative: 'Use GetValuationMultiplesHistory for point-in-time history.' It also provides boundary conditions such as annual-only figures not being TTM, and cohort fallback rules, giving an agent enough context to know if this is the correct data source for a request.

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