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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely useful behavioral content beyond that: the return is scoped per engine, includes each competitor's share of voice against the brand, and includes the delta over the window. No auth or rate-limit notes, but for a read-only analytics tool this is solid.

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?

A single dense sentence with no filler, and the core resource (competitors named in AI answers) is front-loaded. It is slightly run-on, packing scope, per-engine grain, metric, and time comparison into one clause chain, but every element earns its place.

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 return-value burden and does so: competitor identity, per-engine grain, share of voice, and change over window. Combined with annotations covering the safety profile and a 100%-documented schema, an agent has what it needs to call this correctly; only the sibling-routing guidance is missing.

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 three parameters (engines, brand_id, window_days) are already documented in the schema, including defaults and the engines example list. The description adds no parameter-level detail beyond that, so the baseline of 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?

The description states a specific verb+resource: it returns the competitors named in AI answers to the brand's buyer questions, broken down per engine with share-of-voice and change over the window. That is far more precise than a restatement of the name. It does not explicitly distinguish itself from sibling analytics tools like get_visibility or get_citations, so it stops short of a 5.

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?

There is no when-to-use or when-not-to-use guidance and no named alternative. An agent must infer from the name alone that this is the competitive-share tool versus get_visibility (own-brand visibility) or get_citations (source attribution). Purely descriptive, no routing help.

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