Calculate human supervision capacity for an AI agent

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds genuinely useful behavioral context beyond that: the result is an average-based estimate rather than a queueing simulation, which materially affects how an agent should interpret the output. No contradiction with annotations exists.

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 zero waste: the first front-loads the complete set of computed outputs, and the second delivers the usage context and the critical 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 an 11-parameter calculation tool with 100% schema coverage, an output schema, and annotations covering the safety profile, the description is nearly complete. It covers purpose, timing, and the key interpretive caveat. The only minor gap is not pointing to a sibling tool for the queueing-simulation case it explicitly disclaims.

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%, so the schema fully documents all 11 parameters including units, ranges, and meaning. The description adds no parameter-level details beyond naming the computed outputs, so the baseline of 3 applies — the schema does the heavy lifting.

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 uses a specific verb (Calculate) with a clear resource (human supervision capacity) and enumerates five concrete outputs: workload, required FTE, headroom/backlog, monthly labour cost, and sustainable case volume. This output list makes it easy to distinguish from sibling calculation tools like calculate_agent_economics and calculate_evaluation_sample_size, which cover different domains.

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 states when to use the tool ('before production rollout') and what question it answers ('whether the stated human-oversight model is operationally credible'). It also provides a meaningful exclusion caveat ('not a queueing simulation'), though it does not name specific alternative tools for cases where a queueing simulation is needed.

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