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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds only that results are time-series; it says nothing about pagination behavior (which the schema notes is auto-fetched) or result size, so it adds modest value beyond annotations.

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?

Two compact sentences, purpose first and filters second. No filler or redundancy; every clause carries information.

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?

For a read-only list tool with full parameter documentation and no output schema, the description is nearly sufficient; the time-series hint is helpful. It falls short only on sibling disambiguation and pagination/return-shape expectations.

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 100%, with each parameter already documented (ISO 8601 format, enum values, defaults, max 1000 per page). The description's filter list merely restates the schema fields without adding format or semantic detail, so the baseline 3 applies.

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?

States a specific verb (List) and resource (LoS measurement values), and adds the useful qualifier 'time-series' plus the filterable dimensions. It does not differentiate from the sibling list_los_measures (definitions vs values) or get_los_measurement, which an agent must infer.

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 filter list implies how to narrow results but gives no when-to-use, no when-not-to-use, and no routing to alternatives like list_los_measures or get_los_measurement. The agent is left to infer selection entirely.

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