DLP data rule false positives report

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

Detailed annotations already declare read-only/non-destructive behavior, so the bar for additional transparency is lower. The description adds useful behavioral nuance: results are grouped per triggered DLP data rule with endpoint counts, and it clarifies that filtering and pagination are available. No contradictions with annotations.

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 two sentences, front-loaded with the core retrieval action and object, and every clause adds value: the aggregate nature, the filter, and pagination. No redundancy or fluff.

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 read-only report with four optional, well-documented parameters and no output schema, the description adequately conveys the response shape (list of rules with endpoint counts) and options. It could lightly note the default field set or the absence of per-endpoint details, but the given info is largely sufficient for tool selection and invocation.

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?

All four parameters are fully described in the schema (100% coverage). The description's mention of 'filtering by rule class and pagination' maps to ruleClass and page/pageLimit but adds no semantic depth beyond what the schema already provides. It does not introduce param dependencies or special values beyond schema examples.

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 states a specific verb and resource: 'Retrieves false positives reported by endpoints for data rule violations' and clarifies it aggregates counts per rule. This clearly distinguishes it from adjacent DLP network reports (device, email, printer, etc.), which focus on different objects.

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 provides clear context that this is an aggregated false-positive report for data rules and mentions filtering by rule class and pagination. However, it does not explicitly state when to use this over sibling reports or when not to use it, leaving use-case differentiation implicit.

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