feature_importance

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 only names the methods (Random Forest + permutation) but does not mention whether the operation is read-only, what happens to the data, or how results are returned. It adds minimal behavioral context beyond the method names.

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 extremely concise—a single sentence with no fluff. It is front-loaded with the main purpose. However, the brevity sacrifices needed detail, which is a structural tradeoff, but for this dimension the conciseness itself is appropriate.

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 a tool with five parameters and an output schema, the description is severely incomplete. It does not explain the output format, the meaning of the parameters, or the intended use cases. Even with the output schema, the agent would not know typical inputs or how to configure method, sample_size, or feature_columns.

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%, so the description must compensate by explaining parameter meanings, but it does not. None of the five parameters (table_name, target_column, method, sample_size, feature_columns) are described. The description provides no value for parameter understanding.

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 what the tool computes: feature importance using Random Forest and permutation methods. It gives a specific resource and task, but does not distinguish it from sibling tools like correlation_analysis or statistical_test. The purpose is understandable, but lacks explicit differentiation.

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 provided on when to use this tool versus alternatives. The description does not mention when to prefer it over correlation_analysis, statistical_test, or other related tools. There is no context about the expected use case or prerequisites.

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