xai-toolkit

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns descriptive statistics in plain English, which is useful behavioral context. However, it doesn't mention potential limitations (e.g., dataset size constraints), error conditions, or performance characteristics like response time or data freshness.

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: it starts with the core purpose, lists outputs, and provides parameter details with an example. Every sentence adds value without redundancy, and it's front-loaded with essential information.

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 moderate complexity (single parameter, no output schema, no annotations), the description is adequate but could be more complete. It explains what the tool does and the parameter, but lacks details on output format (beyond 'plain English'), error handling, or dependencies on model registration status. For a tool with no structured output schema, more detail on return values would be helpful.

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 description coverage is 0%, so the description must compensate. It provides a clear explanation of the single parameter 'model_id' with an example ('gbc_lubricant_quality'), adding meaningful context beyond the schema's basic string type. This adequately covers the parameter semantics for this simple tool.

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: 'Describe the dataset associated with a model' with specific outputs listed (number of samples, features, etc.). It distinguishes from siblings by focusing on dataset description rather than model comparison, drift detection, or explanation. However, it doesn't explicitly contrast with similar tools like 'summarize_model' or 'list_models'.

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 when needing dataset insights for a specific model, as shown by 'associated with a model' and the model_id parameter. However, it doesn't explicitly state when to use this tool versus alternatives like 'summarize_model' or 'list_models', nor does it provide exclusion criteria or prerequisites.

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