get_model_info

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

Without annotations, the description bears full responsibility for behavioral disclosure. It only states that the tool gets info, but does not mention that it is a read-only operation, whether it requires the model to be loaded, or any side effects. This omission is significant for a tool that accesses model internals.

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 concisely written in a single sentence without waste. However, it could be more informative while remaining concise, e.g., by noting that the output includes model architecture or training metrics.

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 returns detailed information and has no output schema, the description should provide more context about the structure of the returned data. It is incomplete, leaving the agent guessing about the format or contents of the output.

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?

Schema coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning beyond the schema, such as explaining what 'metadata' or 'performance' includes. Baseline is 3.

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 retrieves detailed information about a trained model, including metadata and performance. It distinguishes itself from siblings like evaluate_model or compare_models through the specific verb 'get' and resource 'info', but does not explicitly differentiate.

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 lacks context about prerequisites, such as requiring a trained model, or when to prefer this over get_dataset_info or list_runs.

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