get_industry_employment

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

Since no annotations are provided, the description carries the burden of behavioral disclosure. It does a good job by explaining the default latest-observation behavior, that providing from_date/to_date yields a time series, that adjustment defaults to 'sa', and that geo/state/metro narrow the geography. It stops short of stating data freshness, missing-data behavior, or explicitly calling out read-only semantics, but the output schema covers return format.

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 compact and efficient: two sentences plus a short param list. Each sentence adds value—first states the purpose, second explains default vs. time-series behavior, and the param list clarifies key inputs. There is no fluff or repetition.

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 tool has 7 parameters, no annotations, and an output schema, the description covers the primary use case and main parameters but leaves gaps around geography parameter interactions (e.g., can state and metro be combined? what formats are accepted?). It is adequate for a simple call but not fully comprehensive for a 7-parameter 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?

The input schema has no parameter descriptions (0% coverage), so the description must compensate. It does: it defines naics as the NAICS code, adjustment as 'sa' or 'nsa', dates as YYYY-MM, and explains geo/state/metro as geography narrowers. However, it does not specify the required value formats for state/metro/geo or whether they are mutually exclusive, leaving some ambiguity.

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 opens with a specific action and resource: 'Get employment data for a BLS industry.' This clearly distinguishes the tool from siblings like get_county_employment or get_county_wages by focusing on industry-level employment. It also states the default behavior ('latest observation with year-over-year changes'), adding useful scope.

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 implies usage context by referencing 'BLS industry' and explains when to provide date ranges and how to set adjustment, but it never explicitly states when to use this tool versus alternatives such as get_county_employment or get_occupation_wages. There are no exclusions or alternative recommendations, so usage guidance remains implied rather than explicit.

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