explain_model

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

With no annotations at all, the description carries the full burden, and it does disclose meaningful behavior beyond the schema: the exact hold-out construction (latest 20% by time_col, 20% of groups by group_col, else random 20%) and the max_rows sampling. However, it says nothing about side effects (does it persist a model or artifact?), expected runtime, or the shape of the returned importance list, leaving real gaps for a training tool with zero annotation coverage.

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 purpose question is front-loaded, the hold-out rules are stated compactly, and the Args block adds meaning rather than repeating schema titles. Slightly wordy in the parenthetical hold-out explanation, but every element 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?

For an eight-parameter training/explanation tool with no output schema and no annotations, the description covers inputs and validation strategy well but leaves the return value (ranked feature list? scores?) and any file/artifact side effects undefined. An agent could invoke it correctly but could not predict what comes back.

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 0%, and the description compensates well by glossing all eight parameters, including the allowed values for task ('auto', 'binary', 'multiclass', 'regression') which the schema does not enumerate, and the semantics of max_rows (random row sample) and id_col (excluded from features). The glosses are terse, so it falls short of 5.

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 concrete question ('Which features matter?') and names the specific technique (permutation importance of a gradient-boosting model on a hold-out split), which clearly distinguishes it from siblings like profile_dataset or cv_baseline. It stops short of naming those siblings explicitly, keeping it at 4 rather than 5.

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 phrasing 'Which features matter?' implies the use case (feature ranking after or before modeling), but there is no explicit statement of when to choose this over fit_predict or cv_baseline, and no prerequisites or exclusions. Usage must be inferred from the hold-out methodology described.

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