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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety and idempotency are covered. The description adds genuinely useful context: the auto-selection mechanism (same topic + entity_type), the cross-source reach across six named data providers, frequency auto-alignment to avoid inflated n-counts, and the return shape with confidence labels and sharpen-suggestions. This is well beyond the annotations, warranting a solid mid-high score.

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?

Front-loads the core purpose in the first clause, then layers in auto-selection, return format, frequency handling, and the sibling alternative. Dense but nearly every sentence carries distinct information, with only minor redundancy around candidate omission.

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?

No output schema exists, so the description carries the burden of explaining returns, and it does: a ranking with confidence labels, reason strings, and sharpen-suggestions. Combined with input behavior and the sibling routing hint, an agent has everything needed to call it 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 100%, so the baseline is 3, but the description adds meaning beyond the schema by explaining that auto-selection picks candidates sharing the same topic and entity_type, and that frequencies are aligned to the coarser common grain. These details are not present in the field 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?

States a specific verb and resource ('rank which other metrics best explain the outcome') and frames the exact question it answers. It distinguishes itself from siblings by naming find_drivers as the alternative, so an agent can route without opening any schema.

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?

Explicitly says 'Use this instead of find_drivers when you want a narrative-grade answer', giving both the alternative and the selecting condition. It also clarifies the omission path for candidates, so the agent knows it need not supply them.

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