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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the description need not repeat safety traits. It adds a meaningful methodological caveat—averages vs. queueing simulation—that affects how the agent and user should interpret results, which is valuable beyond the annotations.

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?

Two sentences with no filler: output list is front-loaded, followed by usage context and the single most important limitation. Every clause earns its place.

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 10-parameter calculation tool with full schema descriptions and an output schema, the description supplies the missing high-level context: what the calculation produces, when to use it, and how trustworthy the model is. Nothing essential to invoking it correctly is absent.

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?

With 100% schema coverage, the parameter descriptions already document every input, so the description does not need to explain them. It provides no additional parameter-level guidance such as formulas or interactions, keeping this at the baseline.

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 verb and resource, enumerating five concrete outputs (workload, required FTE, headroom/backlog, labour cost, sustainable case volume). This clearly distinguishes it from sibling calculate_* tools (agent economics, evaluation sample size) without needing to open the schema.

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?

It gives an explicit timing/context trigger: use before production rollout to test operational credibility, and it clarifies the method is average-based, not a queueing simulation. It does not name alternative siblings, but the use case is clearly scoped.

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