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

Annotations already declare readOnly/idempotent/non-destructive, and the description goes well beyond them: default window of 30d, the fact that each timeline is capped with provenance.bounded=true when a cap is hit, and precise error semantics (url_not_joinable vs 'unavailable' with no reason). This is exactly the kind of operational context an agent needs to interpret results and failures.

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 purpose, then layers output shape, window default, cap behavior, and error semantics in dense but information-dense sentences that each earn their place. Slightly long, but no filler or restated boilerplate.

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 full return-value burden — and it does so, describing the four measures with coverage/error data, crawler visit spans, AI source referrals, and citations by date/run. Combined with window defaults and error semantics, it is complete enough to invoke and interpret without further docs.

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 real meaning: the default window (last 30d), the requirement that url come from read_ai_traffic_pages, and the site-membership constraint on url. It adds value beyond the schema without fully re-specifying every parameter.

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+resource (read the AI traffic timeline for one URL) and immediately distinguishes itself from the sibling read_ai_traffic_pages by positioning this as the per-page drill-down. The description even enumerates the four measures returned, so an agent knows exactly what this tool produces.

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?

Gives a clear prerequisite/ordering condition: use this 'after read_ai_traffic_pages names it,' which routes the agent to the right sibling for discovery first. It also states the constraint that the URL must be on the project's own site. There is no explicit when-not-to-use beyond that constraint.

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