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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds two genuine behavioral traits beyond that: results are restricted to published instruments and are ordered best-tested-first. It says nothing about result volume, pagination or what a list entry actually contains, leaving a moderate gap.

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 with no filler and the core purpose front-loaded. The middle clause stacks 'best tested first' and 'optionally by kind or free to use' into an already long sentence, making the ordering guarantee slightly easy to miss.

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?

No output schema exists, but the description compensates by explaining that the list is a discovery surface and that get_evidence supplies per-assessment attributes (test quality, respondent, length, licence, cost). Enough to call it correctly; only result cardinality is left unspecified.

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 coverage is 100% and both parameters are documented in the schema, including the kind key example, so the description carries little extra burden. The phrase 'optionally by kind or free to use' only restates that both filters are optional, adding no syntax or semantics beyond the schema. Baseline 3 applies.

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 (list) and a concrete resource set (published questionnaires, tests and task maps) with the assessed dimensions spelled out: AI readiness, AI literacy, staff attitudes, task suitability. It is clearly separable from find_practices (advice) and get_evidence (per-item detail), so an agent can pick it without opening the schema.

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 routes: use get_evidence to fetch detail on the assessments that fit, and use find_practices when the need is advice on what to do rather than a measurement instrument. The optional filters (kind, free-only) are framed as narrowing the list, giving clear conditions for use.

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