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

The annotations already establish this is a safe, idempotent, closed-world read. The description adds genuinely new behavioral context beyond that: scoring happens 'with fixed rules' (deterministic, no model discretion), each limit carries its underlying rule, gaps come paired with next steps, and the output is explicitly framed as 'a self-assessment' rather than an authoritative audit. That last caveat is exactly the kind of expectation-setting the annotations cannot 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?

Three compact sentences, front-loaded with the core action, then the output contract, then the unanswered-input rule and the self-assessment framing. Nothing is repeated from the schema or annotations and no sentence is padding.

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 output schema, the description carries the burden of describing returns and does so concretely (levels, rules, gaps, next steps). It covers the edge case of unanswered areas and the interpretive status of the result, which is sufficient for an agent to call this correctly after get_readiness_questions.

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 100%, so the schema already documents both the 'answers' mapping (keyed by question id, values being option ids) and the 'lang' enum and its untranslated-text behavior. The description only restates that answers come from get_readiness_questions and hints that partial answer sets are tolerated, which is marginally additive. Baseline 3 is appropriate.

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 (scores) and the exact resource (the answers returned by get_readiness_questions), then enumerates the output shape: a 0-4 level per area, the rule behind each limit, the gaps, and a next step. It is clearly distinguishable from its sibling get_readiness_questions, which produces the input rather than the score.

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 workflow context is explicit: this tool consumes 'the answers from get_readiness_questions', so an agent knows it runs downstream of that tool. It also tells the agent what happens with incomplete input ('Unanswered areas are reported as not assessed'). It does not name any alternative scoring tool or state when not to use it, so it falls short of a 5.

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