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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so the safety profile is fully covered externally. The description adds one useful behavioral fact beyond that – the payloads are "fixed" (static/deterministic) rather than generated – but says nothing about return shape, size, or how many payloads come back.

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?

A single front-loaded sentence naming the artifact, its scope, and its intended use, with no filler or redundancy.

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, deterministic lookup with a fully documented enum parameter and no output schema, the description gives an agent enough to call it correctly. The only minor gap is that it doesn't hint at the quantity or format of the returned payloads.

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% and the single parameter is a fully enumerated category, so the schema already carries the semantics. The description only reinforces that exactly one category's payloads are returned per call (implying multiple calls for full coverage), which is a marginal addition. Baseline 3 applies.

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 resource (fixed prompt injection test inputs) and a precise scope (one attack category) plus the intended consumer (your own chatbot or LLM API). It is clear what the tool returns, though it never names or contrasts itself with the sibling build_test_pack, which sounds adjacent.

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?

"To try against your own chatbot or LLM API" implies the usage context, but there is no explicit when-to-use-this-instead guidance, no mention of when to prefer build_test_pack, and no statement about whether repeated calls are needed for multiple categories.

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