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

Beyond the read-only/idempotent annotations, it discloses rich behavior: no provider call, no credits, reuses already-populated rank_snapshots.serpFeatures, and incurs no new SERP calls. It also explains the null-vs-0 semantics of the metric ('nothing was checked that run... a missing measurement, not a truthful 0% presence'), which is exactly the kind of edge-case behavior an agent needs.

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?

Purpose is front-loaded in the first sentence, followed by cost/model context and the metric definition. It is longer than strictly minimal and the null-handling explanation is dense, but every clause carries useful information rather than restating the name.

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 carries the burden of explaining returns and does so for the key field: aiOverviewPresenceRatePct with its formula and null semantics, plus oldest-first ordering. It stops short of describing the overall series shape or pagination, but is largely sufficient for an agent 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 description coverage is 100%, so all four parameters (including the device enum caveat and sinceDays range) are already documented in the schema. The description's added detail concerns the output metric formula rather than parameter meaning, so the baseline 3 for full schema coverage is appropriate.

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?

It names a specific verb+resource (AI Overview presence trend) and defines the exact scope: share of a tracker's keywords showing Google's AI Overview per check run, ordered oldest first. This is specific enough that an agent can distinguish it from sibling trend tools like get_rank_config_trend or get_tracked_prompt_trend 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?

It clearly signals usage context by stating 'FREE: database only, no SEO data provider is called and no credits are spent,' which tells the agent when this is the cheap option. However it does not explicitly name alternatives or state when-not-to-use relative to other trend tools, so routing is implied rather than enumerated.

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