detect_feature_drift

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the return content ('detailed drift analysis') but lacks critical details: it doesn't specify if this is a read-only operation, what permissions are required, whether it's computationally intensive, or if there are rate limits. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 well-structured and concise. The first sentence clearly states the purpose, followed by details on the return value, and then parameter explanations with examples. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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?

Given the tool's complexity (drift analysis with 2 parameters) and lack of annotations and output schema, the description is moderately complete. It covers the purpose and parameters well but misses behavioral aspects like safety, performance, and error handling. Without an output schema, it should ideally describe the return format more thoroughly, though it does list components like 'statistical test results'.

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?

The description adds meaningful context for both parameters beyond the input schema, which has 0% description coverage. It explains that 'model_id' refers to 'a registered model' and provides an example ('gbc_lubricant_quality'), and clarifies 'feature_name' as 'Name of the feature to analyze' with an example ('mean radius'). This compensates well for the schema's lack of 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's purpose: 'Detect drift for a single feature between training and test data.' It specifies the verb ('detect drift'), resource ('single feature'), and scope ('between training and test data'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'detect_drift' or 'compare_features', which likely serve related purposes.

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 provides no guidance on when to use this tool versus alternatives. It mentions analyzing 'a single feature' but doesn't clarify scenarios where this is preferred over sibling tools like 'detect_drift' (which might analyze multiple features) or 'compare_features'. There are no prerequisites, exclusions, or explicit alternatives stated.

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