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

The description adds valuable behavioral context beyond the readOnlyHint annotation: it states the score is capped at 100, lists the returned fields (raw_ratio, yoy_change, cagr_3y, annual series), and notes the update frequency (annually). This gives the agent a clear picture of what to expect.

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 two sentences: the first defines the output and its components, the second gives use cases and update frequency. It is tightly written with no wasted words and front-loads the core purpose.

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?

The description covers the return values, usage scenarios, and update frequency, which is sufficient for a read-only data tool. It does not explicitly reference the optional 'days' parameter, but the schema fully documents it, so the context remains complete for an agent.

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%, fully explaining the optional 'days' parameter. The description itself does not mention the parameter, but the schema carries the burden. Baseline 3 is appropriate since the description adds no extra info beyond the schema.

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 states it returns a 0-100 US air-travel recovery score based on annual commercial enplanements vs the 2019 baseline, with specific metrics and series. This is a specific verb+resource (returns index) with enough detail to distinguish it from other data tools.

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 lists several user query types and decision contexts: airline traffic recovery, passenger volumes vs pre-COVID, demand trajectory, and timing airline/airport/travel decisions. It lacks mention of when not to use it or alternative tools, so it stops short of a 5.

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