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

Annotations already declare a safe, idempotent, closed-world read, so the safety profile is covered. The description adds that output is interval-based and conditionally limited to measured models, which is genuinely useful, but it says nothing about determinism, whether unmeasured models error or return nothing, or any quota/auth constraints.

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?

A single dense sentence with no filler, and the per-model scoping is front-loaded. It is compact but relies on terse enumeration ("with intervals") rather than a clearly prioritized statement of the primary result.

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 must carry return semantics; it does list the returned quantities, which is the most important part. Still missing are what happens for unmeasured models, output units/format, and whether baseline_cost_estimate_usd is required for net-savings figures.

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 all four parameters are already documented in the schema with examples, formats, and bounds. The description only restates the expected_runs role and the measured-model condition, adding negligible meaning beyond the schema; baseline 3 is appropriate.

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 the concrete computation outputs (expected saving per run, break-even runs, net saving over expected_runs, with intervals), which makes the tool's purpose inferable as an ROI estimation over a capability. However, the verb/action is carried mainly by the title, and nothing distinguishes it from siblings like evaluate_capability or compare_capabilities.

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?

"Savings are reported only for measured models" implies you must supply a model that has been measured and that results are partial otherwise, which is useful context. But there is no explicit statement of when to reach for this tool versus evaluate_capability, compare_capabilities, or get_quote, and no prerequisites such as needing a baseline cost are given here.

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