list_project_learning_result_evaluations

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower. The description adds meaningful behavioral context: the data is 'independent', technical-only, and should not be treated as evidence of broader effectiveness or authority. This goes beyond the structured annotations.

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 short, front-loaded with the primary action, and every sentence earns its place. The caveat paragraph is compact yet high-value for preventing misuse. No redundant filler or restatement of the schema.

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?

With an output schema present and read-only annotations, the description does not need to explain return shapes or safety. It provides enough semantic framing about what evaluations mean and their limits. It could be stronger by differentiating itself from closely related project-learning siblings, but it is not critically incomplete.

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 description coverage is 0%, and the description does not mention project_id or limit at all. While the parameter names are fairly self-explanatory, the description fails to compensate for the missing schema descriptions, leaving an agent to infer how the parameters affect the returned evaluations.

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 opens with a clear verb ('Read') and identifies the resource as 'independent technical replays and separate human decisions', which conveys a specific read operation. It does not explicitly name the sibling list tools, so some differentiation is left to the tool name and schema, but the core purpose is understandable.

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 gives clear interpretive context by stating this is only evidence of passing 'a fixed technical gate' and explicitly lists what it is not proof of. It does not name alternative tools for those other questions, but it provides a strong boundary on when not to over-interpret results.

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