xai-toolkit

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses the two-step process (checking result store, then computing), the statistical tests used (PSI, KS, chi-squared), and the fallback mechanism. However, it doesn't mention permissions, rate limits, or output format details.

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 front-loaded with the core purpose, followed by implementation details and parameter explanation in a structured 'Args' section. Every sentence earns its place without redundancy, making it highly efficient and easy to parse.

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 no annotations, no output schema, and low schema coverage, the description does well on purpose and behavior but lacks details on output format, error handling, and prerequisites. It's adequate for basic use but leaves gaps for an agent to fully understand execution and 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 schema has 0% description coverage, but the description compensates by explaining the single parameter 'model_id' with an example ('gbc_lubricant_quality') and clarifying it refers to a registered model. This adds meaningful semantics beyond the bare schema, though it doesn't detail format constraints or validation rules.

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 with specific verbs ('detect data drift') and resources ('between a model's training data and test data'), distinguishing it from siblings like 'detect_feature_drift' by focusing on overall model drift rather than feature-specific analysis.

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 implies usage for drift detection in registered models but provides no explicit guidance on when to use this tool versus alternatives like 'detect_feature_drift' or 'list_drift_alerts'. It mentions checking the result store first, which hints at performance considerations, but lacks clear when/when-not directives.

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