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

Beyond the read-only, idempotent, non-destructive annotations, the description adds the deterministic nature of the computation and the paid-resource cost ($0.002 USD per call). This gives the agent useful behavioral and cost context beyond what annotations already provide.

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?

Three sentences, each earning its place: the core computation, the intended and prohibited uses, and the pricing. The most important information is front-loaded.

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?

The description covers purpose, exclusions, determinism, and cost, and annotations cover safety. However, with no output schema, it never explains what descriptive statistics are returned, which is a meaningful gap for an agent deciding whether this tool answers its question.

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 carry the parameter-meaning burden. It does clarify that observations are caller-supplied and metric_fields are explicitly named numeric fields, but it does not explain how metric_fields map to observation object keys, missing-data behavior, or accepted value formats.

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 states a specific action ('Computes deterministic descriptive benchmark statistics') over caller-supplied observations and named numeric fields, with an explicit intended use case. It does not, however, distinguish among the several outcome_assurance_benchmark_data_feed_* siblings.

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 explicitly states when to use it ('Intended for outcome assurance / regulatory mismatch') and provides clear exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name alternative sibling tools, so it stops short of full routing guidance.

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