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

Annotations already establish that this is a safe, read-only, non-destructive operation. The description adds substantial behavioral context beyond that: caveats inherited from the map, truncation behavior with truncated and truncated_note, and broken_reason for metrics that cannot be measured as written.

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 dense paragraph with no filler. Each sentence adds meaningful behavior: grounding, caveats, truncation signaling, and broken-metric explanation.

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 no output schema, the description carries the burden of explaining return behavior and does so well by naming truncated, truncated_note, broken_reason, and caveat inheritance. It is complete enough for an agent to understand what this zero-parameter list tool returns.

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 tool takes zero parameters, so the schema cannot document any parameter semantics. Per the rubric, a zero-parameter tool receives a baseline of 4 for this dimension.

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 identifies the resource as grounded KPIs for the workspace and describes what each metric includes, such as map-field grounding and inherited caveats. It does not, however, explicitly distinguish list_metrics from the sibling get_metric or state that it returns a list in contrast to a single item.

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 implies the tool exposes workspace KPIs but never states when to call it instead of get_metric, list_findings, or other siblings. There are no exclusions, prerequisites, or alternative-routing guidance.

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