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

Annotations already establish readOnly/idempotent/non-destructive, so the bar is low; the description nonetheless adds real context: no spending or execution authority, privacy boundaries (exact books and customer jobs stay private), null-on-missing-capability behavior, and precise timing semantics (createdAt→updatedAt, not first-answer or settlement). This is well beyond what the annotations convey.

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 sentences, front-loaded with the verb and scope, each carrying distinct information (scope, timing definition, null behavior, authority/privacy limits). Dense but not padded, though the latency-cohort enumeration is slightly jargon-heavy.

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 input schema fields and no output schema, the description must describe the returned shape, which it does reasonably well (latency cohorts, hold/review rationale, nulls). The only gap is the absence of explicit routing guidance to sibling tools.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No misleading parameter claims are made.

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 ('Observe') and a concrete resource set: public prepaid research operations, queue, completion latency cohorts, and hold/review rationale. It is distinguishable from ledger/outcome siblings by its focus on operational latency cohorts, though it never names a sibling to contrast with.

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 never says when to call this versus keryx_status, keryx_public_job_ledger, or keryx_purchase_outcomes, all of which are adjacent observability tools. Usage is only weakly implied by the scope of what is observed.

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