results_evaluate

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

No annotations are provided, so the description carries the full burden. However, it does not disclose behavioral traits like side effects, permissions, error handling, or whether it supports multiple evaluations. It only states the basic action.

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 follows a clean Arg/Returns structure and is relatively concise. Every sentence serves a purpose. However, the Returns section could be more specific (e.g., format of lists).

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 lack of output schema and annotations, the description provides a baseline explanation of parameters and return values. It is not fully comprehensive; for instance, it does not clarify how errors are returned or the structure of the output lists.

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?

With 0% schema description coverage, the description's Args section adds meaning by explaining each parameter (e.g., examples for expression, unit, dataset, etc.). However, some parameters like 'inner' and 'outer' lack detailed context beyond what is in the schema titles. The compensation is partial.

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 evaluates an expression on a solution dataset. The verb 'evaluate' and resource 'expression on solution dataset' are specific. It does not explicitly distinguish from siblings like results_global_evaluate, but the mention of 'solution dataset' hints at a difference.

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 results_global_evaluate, results_inner_values, or results_outer_values. There are no exclusions or context cues for selection.

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