get_county_wages

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the meaning of location quotients and the industry default, which is useful context. However, it doesn't disclose limitations like data coverage or response format, leaving some ambiguity.

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 three sentences long, front-loaded with the primary action, and contains no filler. Every sentence adds meaningful detail about the tool's output or parameters.

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?

The description covers the core purpose and provides an interpretation of location quotients, but it leaves several optional parameters semantically unexplained. The presence of an output schema covers return structure, but the underdefinition of year, quarter, and ownership reduces completeness.

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 0%, so the description must compensate. It explains fips with a concrete example and clarifies the industry default, but year, quarter, and ownership are left undefined. This is a significant gap for a five-parameter tool.

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 states the tool's function: 'Get a wage snapshot for a US county including location quotients.' It uses a specific verb and resource, and the mention of location quotients distinguishes it from sibling tools like get_county_employment and get_county_unemployment.

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 contextual guidance by explaining what a location quotient above 1.0 means and noting the industry default. This helps an agent understand when to use this tool for county-level wage analysis, though it doesn't explicitly compare to alternatives.

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