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

Annotations cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds real behavioral context beyond them: it enumerates the three return classifications plus the matched rule (valuable since there is no output schema), and discloses that the input description is not stored and that output is guidance rather than legal advice.

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?

Four sentences, front-loaded with purpose and return semantics; each sentence carries distinct information (classification meaning, matched rule, legal disclaimer, storage disclosure). It is slightly dense but nothing is redundant 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?

With no output schema, the description correctly supplies the return-value semantics, and it also covers the privacy question an agent would have about input handling. Input constraints are handled by the schema and safety hints by annotations, so the definition is nearly complete for a single-parameter read tool.

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% and the schema itself already explains the single 'description' parameter and warns against pasting SSNs, diagnoses, account numbers, or passwords. The description's 'what the task is, or what kind of data is involved' essentially restates the schema, so it adds little beyond the baseline.

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 (classify) and resource (a task or data description) against a named ruleset (green/yellow/red for PII and PHI). The three output categories make the tool's function unmistakable and clearly separate it from siblings like assess_ai_readiness or build_governance_policy.

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?

The description implies when to use it by telling the agent to pass a task description or data description, and the red/yellow/green semantics imply the pre-flight use case. However, it names no alternatives or explicit when-not-to-use conditions against the sibling tools, leaving routing to inference.

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