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

Annotations cover the safety profile (readOnly, openWorld, non-destructive), and the description adds substantial behavioral context on top: pricing (US$0.001 per call), a free-tier limit (3 calls/day), no account requirement, and hard input (4,000 chars) / output (256 tokens) bounds. These cost and quota details are exactly the kind of context annotations cannot convey.

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 dense sentences with zero waste: purpose and use cases first, then constraints, cost, and quota. Everything is front-loaded and nothing is repeated.

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?

An output schema exists, so return values need not be explained. The description covers model identity, limits, pricing, and account requirements – everything an agent needs to decide and invoke correctly.

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%, so both parameters are already documented. The description's 'up to 4,000 characters in and 256 tokens out' essentially restates the schema's prompt and max_tokens constraints rather than adding new meaning, so the 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?

States a specific verb+resource ('fast, low-cost AI reply for short jobs') and enumerates concrete use cases (classify, extract a field, rewrite a line, answer a quick question). It names the underlying model (Llama 3.1 8B), which clearly distinguishes it from siblings like talk or web_search.

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 parenthetical examples give a clear sense of when this tool is appropriate – short, bounded jobs rather than open-ended chat. It never names an explicit alternative tool (e.g. talk) or states when NOT to use it, so it stops short of full routing guidance.

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