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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds that data is seasonally adjusted, in thousands of jobs, and monthly from the CES survey—useful behavioral context beyond the annotation baseline.

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 operation, followed by data units, industry categories, and use cases. The industry enumeration is somewhat redundant with the schema but still helpful for an agent to understand mappings without opening the schema.

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 output schema and annotations, the description fully covers purpose, data source, output characteristics, and typical use cases. An agent can invoke it correctly with no missing essential context.

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%, with each parameter already well described. The description redundantly repeats the industry enum mapping but adds no new parameter-level semantics beyond the schema, justifying the baseline score.

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?

Clearly states it retrieves U.S. nonfarm payroll employment data from the BLS Current Employment Statistics (CES) survey. The description names the specific dataset and distinguishes it from sibling BLS tools like bls.macro.cpi and bls.macro.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?

Explicitly says 'Use for labor market analysis, sector employment trends, and economic research,' giving clear context. However, it does not name alternatives or state when not to use, leaving some implicit differentiation from other macro tools.

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