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

Annotations already declare read-only, idempotent and open-world, so the safety profile is covered; the description adds real value beyond that with the methodology (DfE 2023 ranking) and the 'Free, no key' cost/auth note. It also discloses the shape of the result, which matters since there is no output schema.

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?

One dense sentence carries purpose, methodology and outputs, followed by a short access note and a docs link — front-loaded with no filler. The opening sentence is long but every clause contributes.

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 takes on the job of telling the agent what comes back, and it does so at a high level (rating plus task-share plus leading tasks). An agent has enough to call it correctly and interpret the result, though exact output fields are not spelled out.

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 the single parameter is a 22-value enum, so the schema does the heavy lifting. The description only adds the qualifier that occupations are UK-specific, which is a small but useful constraint beyond the schema.

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 (rates) and resource (a UK occupation's AI exposure) plus the concrete outputs: a high/medium/low rating, the share of tasks AI can do, and which tasks it reaches first. That clearly separates it from sibling tools like estimate_ai_staff_impact, though the distinction is left implicit rather than named.

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?

Usage is implied by the description — use it to score a single occupation's AI exposure — but there is no explicit when-to-use or when-to-use-another-tool guidance, and no mention of the sibling calculators an agent should prefer for other questions.

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