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

Annotations already declare readOnly/openWorld/idempotent/non-destructive behavior, and the description adds useful non-obvious traits: computations are 'deterministic' and the resource is paid at a stated x402 price. It does not describe error behavior for missing numeric fields, but the annotation coverage lowers the bar.

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 carry the full payload: computation, intended usage/exclusions, and pricing. The most important verb-and-resource statement is front-loaded, and no sentence is 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 simple two-parameter compute tool with strong annotations, the description is mostly sufficient, but it lacks an output-shape hint and says nothing about how to choose this specific data-feed variant among many similar siblings. The exclusion list and pricing help, but the agent is still left to infer why this feed vs outcome_assurance_benchmark_data_feed_4936a5f9 or cdb4549a.

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 0% schema description coverage, the description compensates by identifying both parameters: 'caller-supplied observations' maps to observations and 'explicitly named numeric fields' maps to metric_fields. It adds the key semantic that metric_fields must name numeric fields, though it does not cover edge cases like missing fields or nesting.

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 a specific verb ('computes') and resource ('deterministic descriptive benchmark statistics over caller-supplied observations and explicitly named numeric fields'), making its function clear. It does not explicitly differentiate this feed from the sibling outcome_assurance_benchmark_data_feed_* variants, so it stops short of a 5.

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?

It gives an intended purpose ('outcome assurance / trust unknown') and an explicit exclusion list ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It stops short of naming concrete alternative tools, so the when-to-use guidance is context-rich but not fully comparative.

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