ologit

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

Annotations declare readOnlyHint=true and openWorldHint=false, which the description does not contradict. The description adds 'via MLE' as an estimation detail but does not elaborate on return behavior, failure modes, or the meaning of 'certified parity evidence', leaving behavioral specifics mostly to the schema's detail parameter and output schema.

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 is concise, but the second sentence ('Validation: certified parity evidence') is a fragment that does not earn its place—it is vague and unexplained. The description front-loads the estimator identity, but the cryptic validation note could mislead or add noise for an agent.

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?

With 14 parameters, 0 required, an output schema, and 100% schema coverage, the description need not enumerate parameters. However, for an estimator named ologit, an agent would benefit from a sentence on when to use it (ordered outcomes with proportional-odds assumption) and what 'certified parity evidence' means—neither is provided.

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 coverage is 100% and most parameters (x, y, tol, alpha, detail, robust, cluster, formula, maxiter, as_handle, data_path, result_id) already have individual descriptions. The baseline is therefore 3; the description earns a 4 because the second sentence implies a validation/parity context that helps interpret the role of parameters like robust and cluster in this specific estimator.

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 identifies the estimator ('Ordered logit (proportional odds) model via MLE') with a specific verb and model type, distinguishing it from related siblings like oprobit and mlogit. However, it does not explicitly contrast with those siblings, and the second sentence about 'certified parity evidence' is cryptic rather than clarifying.

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 implies usage for ordered categorical outcomes but does not explicitly state when to choose this over oprobit, mlogit, or clogit. The 'certified parity evidence' phrase hints at a validation context but gives no concrete guidance on when to invoke this tool versus alternatives.

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