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

With no annotations, the description carries the full burden and does well: it discloses that only counts are returned and never raw values, and it warns that detection is automated and a clean result is not proof of safety. It stops short of saying whether the scanned text leaves the process or is stored, which matters for a PII tool.

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?

Three short sentences, front-loaded with the core behavior, then usage, then the caveat. Nothing is redundant and every sentence carries information an agent needs.

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 and no annotations, the description supplies both the return shape (counts by type) and a reliability disclaimer, which is the essential context for a one-parameter analysis tool. The remaining gap is data-handling behavior, which is left unstated.

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 0% and the single required parameter is undocumented. The description clarifies that the input is the text to be scanned and that its contents are analyzed rather than echoed, but adds no guidance on size limits, encoding, or supported content types.

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 and resource: reporting which personal data types appear in text, with an explicit output shape (counts by type, no raw values). This implicitly separates it from the redact siblings, though no sibling is named outright.

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?

"Use to check whether text is safe to share" gives one concrete when-to-use context. There is no when-not guidance and no routing to alternatives such as redact (for removal) or list_detectors (for what can be detected), leaving the agent to infer those choices.

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