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

Goes well beyond the annotations by disclosing that detection runs on the provider's infrastructure, that content is not sent to a third-party model, that nothing is stored, and that the tool costs credits based on length. It also clarifies the safety-critical behavior that removed values are never returned, only a count by type.

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 tightly packed sentences, front-loaded with purpose and followed by detection scope, return shape, privacy guarantees, and cost. Every sentence earns its place with zero filler.

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?

Although there is no output schema, the description explains the return value (redacted text plus a count by type) and covers privacy, cost, and detection scope. Nothing material an agent needs to invoke the tool correctly 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 the schema already documents all six parameters, including entity types, threshold, and redaction style. The description adds no parameter-level detail beyond what the schema provides, making the baseline 3 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?

States a specific verb (Remove) and resource (personal data from text), and enumerates the detection scope: names, contact details, government/financial identifiers, health identifiers and payment card data. The only sibling, list_entity_types, is a clearly different listing tool, so disambiguation is not needed.

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 clear situational context — use it 'before sending it to a model or storing it' — which tells the agent when the tool is appropriate. It does not name alternatives or state when not to use it, but no meaningful alternative exists among the siblings.

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