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

Beyond the readOnlyHint and non-destructive annotations, the description discloses critical behavioral traits: guest responses return `reason: "guest"` with no data, no memory is kept without a cabinet, the person is inferred from the visitor token (never an id), and `origin: model` traits are guesses worth confirming. This is rich, actionable context that annotations alone do not provide.

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 front-loaded with purpose and content, then usage and guest caveats. It is somewhat long and includes a parenthetical redundancy ('there was nothing to carry'), but every part contributes meaning and no section feels superfluous.

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 must explain the return shape, and it does: up to six traits, last five conversation outcomes, the `origin` field with human/model semantics, and the empty-guest response with `reason: "guest"`. Together with the usage and sign-in instructions, an agent has everything needed to call and interpret the result.

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?

With zero parameters the baseline is 4, but the description adds important semantic guidance: 'The person is taken from the visitor token — never pass an id.' This prevents a common misuse (passing an id) and explains the implicit identity source.

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 opens with a specific verb-and-resource statement: 'What the platform remembers about THIS person between conversations,' and details exactly what is returned (six stable traits and last five conversation outcomes). It is clearly distinct from the write sibling remember_about_person and from knowledge tools like get_agent_knowledge.

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 explicitly says 'Call it before you answer, so you do not ask again what they already told you' and instructs to 'call this door again right after sign-in.' These are clear contextual triggers, though no explicit alternative tool is named or a when-not-to-use case beyond guest behavior.

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