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

Goes well beyond the readOnly/idempotent annotations: it discloses the filter-first-then-rank pipeline, exclusion of unknown-doc endpoints, the never-recommend rule for legacy/deprecated models, that over-budget models are still listed with cost, and clip splitting. These are non-obvious behaviors an agent could not infer from annotations or schema.

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?

Purpose and the evidence list are front-loaded in the first sentence, and subsequent clauses each carry a distinct behavioral fact. It is dense and clause-heavy, but nearly every clause earns its place with a testable rule rather than filler.

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?

For a 6-parameter tool with no output schema, the description fully covers the return shape (rows with proof counts, cheapest $/s, modes, max clip length, split-clip handling) and the ordering/filtering behavior. Nothing an agent needs to call it correctly is missing.

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?

With only 50% schema description coverage, the description compensates materially: it clarifies budget_usd is a total across clips, max_per_shot is per clip, mode is auto-detected from shot text when omitted, and clips/clip_seconds feed the estimate. It adds real semantics beyond the terse schema, though the enum and defaults are already schema-documented.

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 and resource — 'Recommend which AI video model to use for a shot' — and enumerates the evidence returned. It is immediately distinguishable from siblings like get_prices (raw pricing) and get_workflow (process), so an agent knows this is the recommendation engine.

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?

Explains when the tool's filters engage ('Pass budget_usd and/or max_per_shot to drop models whose estimate exceeds it') and the ordering rule that applies otherwise. It gives clear context for invoking it but never names a sibling as the alternative to use when this recommendation is not what's wanted.

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