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.