Monthly hiring plan

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

Annotations already declare a safe, read-only, idempotent, closed-world computation, so the description's burden is lower, and it still adds real behavior: unknown inputs fall back to stated defaults, the response is accompanied by a 15% and 30% attrition sensitivity, and it reports which inputs were assumed. The rule that a hire counts as zero capacity until fully ramped is a genuinely useful behavioral disclosure. It does not cover runtime characteristics such as compute cost or limits on large month arrays.

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?

Purpose is front-loaded in the first sentence, followed by input-mode selection, defaults, sensitivity behavior, and return contents in a logical order with no filler. It is dense but appropriately so for a 14-parameter planning model; only minor tightening would help.

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?

For a 14-parameter computational tool with annotations and an output schema, the description covers the ambiguity an agent would actually hit: default resolution, the two mutually exclusive volume modes, pipeline hires, ramp treatment, and what the response contains. The remaining gap is the absent relationship to its sibling tools.

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 100%, so the baseline is 3; the description earns above that by explaining the interplay of parameters rather than repeating them — monthly_growth is stated to be compounding, current_headcount defaults to month 1's requirement rounded up, and hires_in_flight is framed as the only way to cover months before a new requisition can land. It stays silent on shrinkage, target_occupancy, and hours_per_fte_week, but those are documented in the schema.

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 opening sentence names the resource and the computed output precisely: month-by-month requisitions to open so FTE on hand tracks FTE needed after attrition, time to hire, and ramp. That is far more specific than a tautology. It does not, however, distinguish itself from the siblings required_fte and erlang_staffing, so an agent must infer which of the three to pick.

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?

Guidance is given on how to drive the model (supply weekly_volume with monthly_growth, or weekly_volume_by_month; use hires_in_flight for people already hired) and on default fallbacks for unspecified inputs. What is missing is when-to-use-this-vs-alternatives: no sibling is named and no exclusion is stated, so routing among required_fte, erlang_staffing, and this tool is left implicit.

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