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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and closed-world scope, so the safety profile is covered. The description adds genuine context beyond that: the catalogue contents (key, label, unit, period, direction) and the per-measure county coverage count against a fixed 3,107-county universe, which tells the agent what data usefulness to expect. It stops short of saying whether the list is exhaustive or paginated, but the fixed domain implies a bounded result set.

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 covering contents, scope, and the sole parameter, followed by a two-word note on the filter. Front-loaded with the verb and resource, zero filler.

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 single-optional-parameter, read-only listing tool with no output schema, the description covers purpose, returned fields, dataset size, and filter optionality, which is close to sufficient. The remaining gap is what the filter actually matches on, which the agent needs in order to use it well.

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% and the single 'filter' parameter has only type and maxLength constraints, so the description's 'Optional text filter' adds a little meaning: it is optional and it is textual/substring in nature. It does not clarify which fields are searched or whether matching is case-sensitive, leaving the schema slightly under-served.

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 resource ('county measures'), and even enumerates the returned fields (key, label, unit, period, direction, coverage count) plus the dataset scope of 3,107 counties. It does not name or contrast with the nearest sibling, find_measure, which is also measure-oriented, so the list-versus-lookup distinction must be inferred.

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 only usage signal is 'Optional text filter', which hints that you can narrow results by text but never says when to call this versus find_measure, whether it should be called first to discover keys, or whether the filter matches key, label, or both.

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