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

Annotations already declare the write profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false), so the safety bar is partly cleared for the description. The description adds real behavioral context beyond that: partial-update semantics for omitted fields, the downstream fan-out into keyword and competitor generation, and the warning that vague input degrades outputs. It does not describe the response shape or failure behavior, which is the remaining gap.

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 and the highest-leverage rationale are front-loaded, followed by the call sequence, then the worked example. The example is long but every part of it demonstrates a distinct field with distinct reasoning, so it earns its place; the all-caps emphasis line is slightly shouty but is the single most operationally important instruction in the text.

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?

For a six-parameter mutation with no output schema, the description covers what is written, how partial updates behave, what depends on the data downstream, and how to phrase each field. The only omissions are the return value and what happens on invalid or rejected input, which are minor relative to the richness provided.

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 baseline is 3, but the description goes well beyond the schema by supplying a fully worked, field-by-field example for an AI-governance vendor that shows the expected altitude, phrasing and content of each value. The instruction to write in the buyer's language and to split multi-motion businesses into separate keyword sets adds semantic guidance the schema descriptions only gesture at.

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 ('Set') and resource ('a brand's positioning profile') and enumerates exactly which fields constitute it: industry, USP, ICP, target audience, summary. It also implicitly separates itself from the sibling get_brand_profile by naming it as the prerequisite read step, so an agent can route between the read and write pair without opening either 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?

Gives an explicit call sequence ('Call get_brand_profile first and pass back the fields you are changing') and states the alternative behavior for the omitted case ('omitted fields are left as they are'). It also explains the justification for using the tool at all — that downstream keyword research, competitor discovery and AI-visibility prompts are generated from this data — which tells the agent when this tool is the right investment of effort.

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