nbreg

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

Annotations include readOnlyHint=true, so the safety profile is already covered. The description adds useful behavioral context by documenting the formula convention 'y ~ x | id' for fixed effects and by stating 'Validation: certified parity evidence,' which signals that results have been verified against a reference implementation.

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 short, front-loaded with the core purpose, and contains no filler. It uses each sentence productively to add use-case, formula, and validation context. It stops short of a 5 because the validation phrase is terse and leaves 'certified parity evidence' somewhat unexplained.

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 13-parameter tool with a rich output schema and 92% schema parameter coverage, the description provides enough context for an agent to select and invoke the tool correctly. It covers the model family, use case, formula pattern, and validation status. It does not enumerate all parameter combinations, but that is not necessary given the schema richness.

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 92%, so the schema already documents most parameters. The description adds value by illustrating the formula syntax for fixed effects, which clarifies the 'formula' parameter, but it does not add meaning to the other parameters beyond what the schema provides.

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: 'Fit a negative-binomial count model.' It also names the target data type ('overdispersed non-negative count outcomes'), which helps distinguish it from Poisson or other count models, and gives a concrete formula example.

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 provides clear context on when to use the tool ('Use this for overdispersed non-negative count outcomes') and hints at the fixed-effects formula pattern for moderate panels. However, it does not explicitly name alternative tools such as poisson, zinb, or xtnbreg, nor does it state when not to use it.

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