get_feature_importance

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

With no annotations, the description carries the full burden and does well: it discloses the ranking metric (mean |SHAP|), that scores are averaged over a sample rather than a single prediction, and the per-entry payload (raw name, friendly name, importance score). It does not mention permissions, rate limits, or whether the sample is configurable, but for a read-only ranking tool the substantive behavioral context is present.

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?

Front-loads the core purpose, then the disambiguation, then the return shape, then the arg — a clean information hierarchy with no filler. Every clause earns its place and the Args block is conventional and scannable.

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?

An output schema exists, so the return contract need not be re-explained, yet the description still summarizes it usefully; the single optional parameter is documented and the sibling distinction is drawn. Nothing an agent needs in order to select and call this tool is missing.

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 0% and the schema carries only a default with no description, so the description must compensate, and it does: 'top_n: how many features to return (default 10)' explains both the meaning and the fallback behavior. It is complete for the single parameter, though it adds no guidance on sensible ranges or interaction with the underlying sample.

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?

States a specific verb and resource ('Return the model's globally most important features') and immediately scopes it with the SHAP methodology. It explicitly distinguishes itself from the sibling concern of per-borrower explanations ('as opposed to the reasons behind one borrower's score'), so an agent can route correctly without opening other schemas.

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?

Gives an explicit when-to-use heuristic ('what drives this model overall? questions') paired with the contrasting case ('the reasons behind one borrower's score'), which maps directly onto the sibling explain_prediction. The routing decision is fully spelled out rather than left to inference.

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