US Minimum Wage by Address and Date

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

With no annotations provided, the description carries the full burden and does well: it discloses that the returned rate is the highest of federal/state/county/city, that employer_size changes the shape of the answer (one rate vs. every variant listed), that future dates use scheduled/announced rates, and that addresses are resolved via the US Census geocoder. It does not cover error behavior for unresolvable locations or rate limits, so not a 5.

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?

Front-loaded with the core verb and resource, then layers in scope, input mechanics, and the free note in three tight sentences. The parenthetical examples (California fast food, Los Angeles hotels, unincorporated areas) are dense but each earns its place by clarifying edge cases; only slight trimming would improve it.

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 an 8-parameter, zero-required tool with no annotations and no output schema, the description covers the return contents, input strategy, geocoding method, and variant behavior well. Missing details are minor: no guidance on what happens when a location cannot be resolved and no note on result shape beyond the listed rate components.

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 already 100%, so the baseline is 3, but the description adds real value beyond the schema: it explains why address is most accurate (resolves the exact city and county, including unincorporated areas), what employer_size does to the result (single answer vs. listed variants), and that industry flags special rates. These are behavioral consequences of the parameters, not restatements of field descriptions.

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 and resource (returns the legal hourly minimum wage) and enumerates exactly what comes back: the applicable rate, the setting jurisdiction, tipped cash wage, industry rates, next scheduled change, and sources. This is clearly distinguishable from list_upcoming_minimum_wage_changes, which only lists future changes rather than computing an applicable rate for a location and date.

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?

Gives concrete input guidance: supply an address for the most accurate resolution, or state plus city/county otherwise, and pass employer_size to collapse variants into a single answer. It adds a 'Free' note that helps the agent decide whether to call. It stops short of explicitly naming the sibling tool or stating when not to use this one, so it sits just below full marks.

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

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

No annotations are provided, so the description carries the full behavioral burden. It adds useful context by stating the tool is free and that the returned data includes a 'last checked' date (implying freshness), but it omits other behavioral details such as read-only safety, authentication requirements, or rate limits. This is adequate but not comprehensive for the no-annotation case.

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?

The description is a single efficient sentence that front-loads the core purpose ('Lists coverage') and then adds the key details without any wasted words. It is appropriately sized for a simple list tool.

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?

Given the low complexity (zero parameters, no output schema, no annotations), the description is nearly complete: it explains what is returned and notes the tool is free. It could be slightly richer by stating that it is a read-only discovery operation or suggesting when to call it, but for a simple capability listing it provides enough context to use correctly.

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?

The tool takes zero parameters, so there is no parameter semantics to compensate for. The baseline for zero parameters is 4, and the description appropriately does not need to discuss inputs.

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 states a specific verb (Lists) and resource (coverage) and details exactly what is returned: state/county/city counts, last-checked date, and related Nero Labs Rules servers. It is clear what the tool does, but it does not explicitly differentiate itself from the sibling wage-query tools (get_minimum_wage, list_upcoming_minimum_wage_changes), so it falls short of a 5.

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 tool versus alternatives. The description never mentions the sibling tools, nor does it suggest contexts such as 'call this first to discover available coverage before querying wages.' It only describes what the tool returns.

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

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

With no annotations, the description carries full behavioral burden. It does disclose useful traits: ordering (soonest first), returned fields (new rate and source), and cost/auth posture ('Free'). It omits pagination, result caps, and whether announced-but-unscheduled changes are included, so it is adequate but incomplete.

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 short sentences, front-loaded with the core action and scope, then usage, then the state filter and cost note. Every sentence carries information, with only mild promotional tone in 'warn an employer'.

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 simple optional-parameter list tool with no output schema, the description covers what is returned (new rate and source), ordering, geographic coverage, and cost/auth posture. Pagination behavior is the notable remaining gap, but nothing essential to invoking it is missing.

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 days, state, and from_date fully. The description adds only framing ('in a date window', 'Filter by state') without extra syntax or format detail, which matches the baseline 3 when the schema does the heavy lifting.

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 states a specific verb and resource ('Lists every scheduled or announced minimum wage change') with clear scope (state, county and city) and ordering ('soonest first'). It doesn't explicitly name get_minimum_wage as the contrasting sibling (current rates vs. upcoming changes), so differentiation is left partly to inference.

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?

It gives a use context ('plan payroll or warn an employer before a rate rises'), which implies when to reach for it. But there is no explicit when-not guidance and no named alternative such as get_minimum_wage for current in-force rates, leaving the sibling choice ambiguous.

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