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

With readOnlyHint=true already declaring the safety profile, the description adds meaningful context beyond annotations: it specifies exactly what the simulation returns per answer. It does not address determinism, cost on large step sequences, or statefulness, but the return-shape disclosure is substantial.

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?

Three sentences, front-loaded with what the tool does before the return values and the recommended use. The middle sentence is long and comma-heavy, but every clause carries information; the 'instead of reimplementing it' closing line 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?

There is no output schema, and the description compensates by naming the four return components an agent needs to interpret results. The one gap is that it describes 'a study mode' as if always supplied, though mode is optional in the schema, leaving the default behavior unstated.

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 already defines every parameter, including the hint that 'hinted' cools more weakly. The description restates 'terms', 'mode', and 'sequence of answers' but adds no semantics beyond the schema, and never mentions 'starting_level'.

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?

States a specific action and resource: run the real K2 engine over a caller-supplied item pool. It also enumerates the outputs (p-value trajectory, mastered items, pool expansion, next-item distribution), which makes it clearly distinct from the explanation-oriented sibling explain_k2_algorithm.

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 final sentence gives a concrete use case: drive your own drilling with K2 as the scheduler rather than reimplementing it. It gives clear situational context but names no alternative tool by name and states no exclusions or limits.

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