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

With no annotations, the description carries full burden and excels: it discloses that 'Values are modelled estimates, not company-reported figures,' that panel backfill means 'any lookback the panel cannot support returns null with a reason — never an interpolated number,' and that ticker ambiguity is handled by naming the entity and offering employer_id for pinning. These are significant behavioral traits beyond basic read-only semantics.

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 a single dense paragraph but every sentence earns its place: purpose, return metrics, use case, data caveats, null behavior, and ticker ambiguity. It is front-loaded with the core purpose and structured logically, making it easy to parse despite the length.

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 tool has no output schema, so the description must explain return values; it does so comprehensively, listing all key metrics (headcount, rates, growth, salary, full series) and coverage. It also addresses data quality, null handling, and entity disambiguation, making it complete for an agent to invoke 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?

Schema coverage is only 33%, so the description compensates by explaining employer_id ('pin with employer_id') and clarifying ticker ambiguity ('Ticker is not unique across venues'). It does not explicitly describe the 'months' parameter, but the schema provides default/min/max and the description mentions 'lookback,' giving partial context.

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 provides 'Monthly EMPLOYMENT MOMENTUM for one company' with specific metrics like headcount trend, hiring/attrition rates, and salary level. It distinguishes itself by emphasizing 'for one company' and 'monthly workforce panel rather than an annual filing,' differentiating it from screen and fundamental filing tools.

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 says 'Call it to see a company expanding or shrinking MONTHS before the next 10-K, or to catch attrition rising while headcount looks flat,' providing clear use cases. However, it does not explicitly name alternative tools or state when not to use it, though 'for one company' implicitly distinguishes it from screening tools.

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