model_diagnostics

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently lists the computed diagnostics and plots, but omits whether the tool mutates the model, what model types it supports, or whether results are printed or returned. These gaps matter for an analysis tool.

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 a single, front-loaded sentence that leads with the core purpose and then enumerates the report components. Every listed item is specific and informative, with no filler or repetition.

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?

The description adequately conveys the tool's scope, but without an output schema or annotations, it should clarify supported model types and the form of the returned report. The gap is significant given the complexity of the diagnostics and the absence of other structured metadata.

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%, giving a baseline of 3. The description adds meaning by clarifying that 'influence_n' refers to cases identified by Cook's distance and leverage, and that 'plots' relates to the four standard diagnostic plots. This goes beyond the terse schema descriptions.

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 clearly states the tool produces a 'full diagnostic report for a fitted model' and enumerates the specific diagnostics included. It distinguishes itself by naming concrete tests and plots, though it does not explicitly contrast with sibling tools like check_assumptions.

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?

No guidance is given on when to use this tool versus alternatives such as check_assumptions or model_compare. The context is implied to be post-fit diagnostics, but the description never states a preferred scenario or exclusion criteria.

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