model_plots

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

While annotations already declare readOnlyHint=true and destructiveHint=false, the description adds valuable behavioral context beyond annotations: it warns about the potentially large size of pr_curve data, explains that field shapes vary by type (pr_curve has ap, confusion_matrix has matrix), and clarifies that plot presence does not correlate with training history. This is rich, non-obvious behavior that an agent needs to know.

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 detailed but every sentence earns its place. It front-loads the purpose and key distinctions, then adds essential operational details (size warning, field structures, presence caveats) without redundancy. It is well-structured and not overlong.

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 read-only tool with no output schema, the description is remarkably complete. It covers what the tool returns, how to filter, the variability of fields, and edge cases like empty plots. An agent has everything needed to call it correctly.

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 already has 100% coverage with descriptions for all three parameters, so schema is doing the heavy lifting. However, the description adds meaningful semantics about the 'type' parameter (e.g., that it returns data unmodified, and that field shapes vary), which goes beyond simple parameter definitions, earning a 4.

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 it reports evaluation plots with specific plot types listed, and differentiates from model_metrics and training_monitor by noting they do not surface these plots. This is a specific verb-resource pair with clear sibling 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?

The description explicitly contrasts with model_metrics and training_monitor, telling the agent when to use this tool instead. It also provides clear guidance on default behavior vs. passing 'type', which is a usage guideline in itself.

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