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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable context: the official establishment survey covering 650,000 US worksites, the Federal Reserve usage reference, source attribution, trend classification, and a concrete example. It does not discuss data lag or revisions, but none are safety-critical for a read-only retrieval.

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 front-loaded with usage intent, then delivers the return value, source, example, and cost without redundancy. The example is especially efficient, showing the exact shape and interpretation of the data an agent should expect.

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?

With a single required enum parameter, readOnly annotations, and no output schema, the description fully specifies what the tool returns: employment level, MoM change, YoY change, and trend classification, including a concrete example. An agent has enough information to select and invoke this tool correctly.

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 schema has 0% description coverage, so the description carries some burden for explaining the 'sector' parameter. It says 'by major sector' and gives a Healthcare example, which adds context, but it does not enumerate the allowed sector values or explain options like 'all_private'; the enum schema remains the primary source of parameter meaning.

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 states a specific resource and action: it returns BLS payroll employment by major sector with MoM change, YoY change, and trend classification. This clearly distinguishes it from sibling labor-market tools like get_bls_inflation_components or get_employment_cost_index.

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 explicitly lists four use cases: workforce planning benchmarking, industry growth assessment, board reporting context, and talent acquisition timing. It does not explicitly name alternatives or state when not to use the tool, but the use-case framing is strong enough for an agent to route correctly.

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