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

The description adds significant behavioral context beyond the readOnlyHint and destructiveHint annotations: it discloses cost (1 IU per call, plus LLM token accrual), performance (millisecond-scale round-trip), and the sealed-engine attestation path. No contradiction with annotations.

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?

The description is front-loaded with the core purpose, then organized into labeled sections (Purpose, Use when, Do not use when, Capability class(es), Path fit, Cost). Every section adds distinct information without redundancy, making it efficient despite its length.

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, usage guidance, alternatives, cost, deployment options, and capability class. With an output schema present and rich annotations, there are no significant gaps for an AI agent to invoke the tool correctly.

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 schema already fully describes the sole parameter 'values' as '50 floats' and explains the internal rendering. The description's first line reiterates '50-value feature vector' but does not add new semantic information. With 100% schema coverage, the baseline is 3.

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 opens with 'Score a 50-value feature vector against the legacy /v1/infer route on the sealed engine,' which is a specific verb ('Score') and resource ('feature vector','legacy /v1/infer route'). It further distinguishes itself from siblings by explicitly labeling it as 'legacy' and recommending kirk_score_book for fresh integrations.

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 includes explicit 'Use when' and 'Do not use when' sections, stating to use it for existing clients wired to /v1/infer and to avoid it for fresh integrations or tight loops, with named alternatives (kirk_score_book, kirk_bulk_howto). This is clear, actionable guidance with alternatives.

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