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

With no annotations or output schema, the description carries the full burden. It usefully discloses that the tool is free, defines the statistic (median full-time gross annual pay), and names the data source (ONS ASHE), but omits caveats such as small-area suppression, postcode-to-local-authority mapping approximations, currency, and return format.

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?

A single compact sentence plus the 'FREE' marker, front-loaded with the cost signal and then the substance. It is efficient, though 'FREE.' is stylistically awkward.

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?

For a one-parameter lookup tool, the description gives enough to select it, but with no annotations and no output schema it should say more about what comes back (a single number? GBP? missing-data behavior) and any input format expectations.

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 0%, so the description must compensate. It adds meaningful context by saying the input is a UK postcode that is resolved to a local authority, but does not specify postcode format, case, or precision expectations beyond 'UK postcode'.

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 precisely what is returned: median full-time gross annual pay for the local authority area of a UK postcode, citing ONS ASHE. This is a specific metric and scope that an agent can distinguish from salary_percentiles, though it does not name that sibling explicitly.

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?

There is no guidance on when to use this versus salary_percentiles, compare_areas, or offer_check. The description leaves the agent to infer the use case from the metric alone.

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