fracreg

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

Annotations already declare readOnlyHint=true, so the description does not need to repeat safety behavior, but it also adds almost no operational context. 'Validation: certified parity evidence' is cryptic and does not explain convergence behavior, quasi-MLE robust errors, caching, or payload-detail behavior. This falls short of even a lowered bar for annotation-covered tools.

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 very short and front-loaded with the model name, which is structurally concise. However, the second clause about 'certified parity evidence' is vague and does not clearly earn its place, and the overall brevity leaves important selection and usage information absent.

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?

This is a 14-parameter tool with no required fields, multiple data input modes, and many closely related sibling tools, yet the description provides only a model label. An agent would have to rely entirely on the schema and output schema to understand data requirements, alternatives, and invocation patterns, which is inadequate for the tool's complexity.

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 every parameter is already documented with types, defaults, and descriptions. The tool description itself adds no parameter-level meaning, which matches the baseline expectation when the schema carries the full burden.

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 names a specific model type, 'Fractional response model', with a canonical citation, so an agent can infer the tool estimates a fractional response model. However, there is no explicit verb like 'estimate' or 'fit', and nothing distinguishes fracreg from sibling models such as betareg, logit, or probit for bounded outcomes. The appended 'Validation: certified parity evidence' is not a functional description of the operation.

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 no guidance on when to use fracreg versus alternatives like betareg or logit, and no conditions or exclusions are stated. Usage context is only implied by the model name and the outcome range documented in the y parameter schema, not by the description itself.

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