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

No annotations are provided, so the description carries the behavioral disclosure burden. It clearly indicates a read/return operation and lists the metric types, but it does not disclose output format, caching, or any side effects. The behavior is not misleading, just minimally elaborated.

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 compact, front-loaded with the core action, and every sentence adds value. The argument list is a clear, scannable enumeration without 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?

This is a simple single-parameter tool with an output schema present, so the return shape does not need to be in the description. The description supplies the one critical piece of information—the valid metric values—making it complete enough for correct invocation.

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 schema only defines 'metric' as a string with a default. The description compensates fully by enumerating all valid metric values, so an agent knows exactly what to pass.

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 uses a specific verb ('Return') and a clear resource ('GDEX portal-wide metric'), and enumerates the exact metric options. This distinguishes it from dataset-level siblings like get_dataset_metrics.

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 phrase 'portal-wide' implies when to use this tool versus dataset-specific tools, but there is no explicit when-to-use/when-not-to-use guidance or mention of alternatives. Usage context is present but left to inference.

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