summarize_model

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 the return format ('model type, accuracy, number of features, and the top features ranked by importance — all in plain English'), which is helpful behavioral context. However, it does not mention potential limitations like model availability, permissions needed, or error conditions.

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 return details and parameter explanation in a clear, bullet-like structure. Every sentence adds value without redundancy, making it 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?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is largely complete: it covers purpose, returns, and parameter meaning. However, it lacks details on error handling or dependencies, which would be beneficial for robust agent use.

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 the single parameter: it explains that model_id refers to 'ID of a registered model' and provides an example ('gbc_lubricant_quality'). Since schema description coverage is 0% (no schema descriptions exist), this compensates well, though it could specify format constraints like length or allowed characters.

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 specific action ('Summarize what a model does and what drives its decisions') and the resource ('a model'), distinguishing it from siblings like list_models (which lists models) or explain_prediction (which explains specific predictions). It provides a concrete verb and target.

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 you need a high-level overview of a model's characteristics and feature importance, but it does not explicitly state when to use this tool versus alternatives like get_skill (for skill metrics) or explain_prediction (for individual predictions). No exclusions or prerequisites are mentioned.

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