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

Annotations already carry the safety profile (readOnlyHint, idempotentHint, openWorldHint, destructiveHint=false), lowering the bar. The description adds valuable non-schema context: determinism, the caller-supplied scope of the computation, and the metered cost ($0.002 USD per call at the direct resource URL). No contradiction with annotations — 'deterministic' is consistent with the idempotentHint.

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?

Four tight sentences with the core function front-loaded. The pricing disclosure and prohibition list each earn their place. Minor redundancy: 'outcome assurance' in the intended-use sentence duplicates the tool's own taxonomy, and the six-item prohibition list could be compressed, but there is no 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 two-parameter, compute-only tool with rich annotations, the description covers what it does, over what inputs, in which domain, when not to use it, and what it costs. Since no output schema exists, a note on the return shape would be helpful, and the observations↔metric_fields linkage plus edge-case behavior remain unexplained — but an agent can select and invoke this tool with reasonable confidence.

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 compensate, and it partially does: 'caller-supplied observations' clarifies the data source and 'explicitly named numeric fields' clarifies the metric selector. But it never states that metric_fields entries must be property keys present inside the observation objects, nor how missing or non-numeric fields are handled. The compensation is partial at best.

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 ('computes') and resource ('deterministic descriptive benchmark statistics' over 'caller-supplied observations and explicitly named numeric fields'). This clearly distinguishes the tool from verification/orchestration/policy/routing siblings by both function and scope. However, it does not differentiate among the five other outcome_assurance_benchmark_data_feed_* siblings, which share the same domain and appear interchangeable.

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?

Explicitly names the intended use ('outcome assurance / latency excess') and provides a substantial when-not list ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). This is clear contextual guidance, but it falls short of a 5 because it never names an alternative sibling to route to for those excluded use cases.

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