cloud-audit

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

No annotations are provided, so the description carries the full disclosure burden. It provides good transparency about the return values (0-100 scale, USD currency, specific counters), but lacks other behavioral details like whether this is a cached calculation, real-time query, or if it requires specific permissions.

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?

Two concise sentences with no waste: first states the operation, second details the return payload. Well front-loaded and appropriately sized.

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 parameterless tool with an output schema, the description appropriately supplements the structured output by documenting the value ranges (0-100), units (USD), and specific counters included, providing sufficient context for an agent to understand the tool's utility.

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?

Input schema contains zero parameters. According to scoring rules, 0 parameters establishes a baseline score of 4.

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 clearly states it retrieves the 'current health score and risk exposure summary' with specific metrics (0-100 score, finding counts, attack chain count, USD exposure). However, it doesn't explicitly differentiate this aggregate view from sibling tools like get_findings or get_attack_chains which return detailed data on similar concepts.

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?

No guidance provided on when to invoke this versus siblings. It describes what data is returned but doesn't clarify use cases (e.g., 'use this for executive dashboards' vs 'use get_findings for detailed analysis').

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