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

Since no annotations exist, the description carries a heavy burden. It effectively discloses behavioral traits by listing specific rate categories (youth, small employer, industry) and the fact that every rate links to an official source, which is vital behavioral context for legal data.

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 dense but extremely efficient, front-loading the definition of the wage scope and listing all associated types of rates in one paragraph without unnecessary 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?

Given the complexity of minimum wage law, the description covers the essential missing pieces: the geographic scope (US/Canada), the temporal scope (next scheduled change), and the detailed output metrics (tipped vs. standard rates). It is highly complete for a legal data tool.

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?

With schema coverage at 71%, the description provides necessary semantic depth by specifying the acceptable location inputs (address, lat/lon, or tiered geographical structure) and the data source requirement (US/Canada), thereby adding meaning beyond the simple list of parameters.

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 clearly specifies the resource and the breadth of data provided: federal, state, county, city rates in the US, Canadian province rates, tipped cash wages, and next scheduled changes. It leaves no ambiguity about the tool's core function.

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 description provides strong input guidance by outlining acceptable location formats (address, lat/lon, state/county/city). However, it lacks explicit guidance on when to use this tool versus alternatives like minimum_wage_changes, which is a common confusion point for this tool.

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