get-models-scores-training-dataset-by-model-id

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation ('Get'), but doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what the output format might be. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly, with no wasted verbiage or structural issues.

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 and no output schema, the description is insufficiently complete. It lacks details on behavioral traits (e.g., authentication needs, rate limits), output format, and differentiation from siblings. While concise, it doesn't provide enough context for an agent to fully understand how to use this tool effectively in a complex environment with many similar tools.

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 input schema has 100% description coverage, with the single parameter 'modelId' documented as 'The training dataset scores' modelId'. The description adds no additional semantic context beyond this, such as explaining what a 'modelId' is or how to obtain it. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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 action ('Get') and resource ('training dataset scores') with a specific identifier ('for the given modelId'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get-models-scores-prompt-by-model-id' or 'get-models-by-model-id', which also retrieve model-related data, leaving some ambiguity about what exactly 'scores' refers to in this context.

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, such as other 'get-models-*' siblings that might retrieve different aspects of model data. It lacks context about prerequisites, typical use cases, or exclusions, offering only a basic functional statement without strategic direction.

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