get_time_remaining

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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered. The description adds crucial semantics: None values during analytics warm-up do not mean unlimited, preventing a common misread. This is meaningful behavioral disclosure beyond the structured metadata.

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 and well-ordered: purpose, output, alternative, caveat, and args. The [READ] prefix signals intent. Every sentence contributes, with no fluff.

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 adequately describes the return structure (per-dimension entries with projected days) and the warm-up caveat. It leaves some depth unexplained (e.g., calculation method), but that's not essential for an agent to select and invoke the tool correctly.

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 coverage is 0%, but the description explains both parameters: resource_id is the resource UUID with an example type, and target is the Aria target name from config with default when omitted. This compensates for the barren schema, though it could be more specific about target's source.

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 action: 'Predict when a cluster will exhaust its capacity based on usage trends.' It also specifies the return payload (time_remaining per dimension) and explicitly contrasts with sibling get_capacity_overview, making the tool's unique scope unmistakable.

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?

Directly instructs: 'Use get_capacity_overview instead when you also want current headroom' and notes this tool returns only projections. This gives an explicit decision rule and names the alternative.

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