results_global_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. It states the tool returns a single numerical value but does not disclose side effects (likely read-only), performance implications, or prerequisites (e.g., model must be loaded). The description lacks behavioral details beyond purpose.

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 concise: a one-sentence purpose, bullet-point examples, and clear parameter documentation. It is front-loaded with the primary action. Every sentence is useful and there is no redundancy.

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 has 4 parameters, no output schema, and no annotations, the description covers the basics but lacks important context such as supported expression syntax, accepted units, whether a model must be loaded, or if it triggers computation. It is adequate but not comprehensive.

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%, so the description adds value by explaining each parameter: expression as 'Global expression to evaluate', unit as 'Desired unit for result', dataset and model_name with defaults. Examples of expressions further clarify. While not extremely detailed, it compensates well for the missing schema descriptions.

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 a global expression returning a single scalar value, with specific examples of expressions like integration, maximum, and derived values. However, it does not explicitly differentiate from sibling tool 'results_evaluate', which may also evaluate expressions but likely on a local scope.

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 common usage examples but lacks explicit guidance on when not to use this tool or alternatives. It implies use for global expressions but does not exclude other tools like 'results_evaluate' or 'param_get'.

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