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

Annotations only declare readOnlyHint and openWorldHint, so the description carries weight here. It adds concrete cost and pricing information ($0.003 base, per-text and per-character charges, $0.03 USDC via x402), which goes well beyond annotations. It doesn't state rate limits or failure modes, but the cost transparency is valuable.

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 sentences, front-loaded with the core purpose and the usage cue, then pricing details. No filler, though the pricing string is dense.

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?

Covers purpose, hazard taxonomy, usage trigger, and cost, which is comprehensive for a classifier with no output schema. Missing details on how to interpret batch output or why role matters beyond the schema hint.

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 only 33%, so the description partially compensates by noting the 10-text limit and that role can be user/assistant, but the text vs texts parameters are not distinguished and the description doesn't clarify their relationship (single vs batch). Baseline 3 given partial schema coverage.

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 (texts) with scope ('up to 10'), the underlying model (Llama Guard 3), and the output labels (safe/unsafe with hazard categories). Distinguishes it from the many sibling tools via the safety-classification framing.

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?

Provides clear context for use: 'Check user input or your own reply before posting.' This tells the agent when to invoke it, but does not name an alternative sibling (e.g., moderations, classify) or state exclusions.

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