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

With no annotations, the description carries the behavioral disclosure burden. It does state the output type ('text completion'), which is a useful behavioral trait. However, it omits other behavioral context such as latency, cost, determinism, or that the model parameter affects results. The description is not misleading, but it is minimal.

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 short sentences with no filler. The core action is front-loaded ('Ask an LLM a question') and the return type follows immediately. Every word earns its place.

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 simple two-parameter tool with no output schema, the description covers the basic contract. However, with no annotations and no guidance on model selection or prompt expectations, the agent has to infer some behavior. It is adequate but not rich.

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 50%: only 'prompt' is described ('The user prompt'), while 'model' has an enum but no explanation. The description itself adds no parameter semantics—it does not clarify what 'model' means, what the options represent, or what the default does. Since coverage is low, the description needed to compensate but did not.

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 uses a specific verb ('Ask') with a clear resource ('an LLM'), and explicitly states the result ('Returns a text completion'). This clearly distinguishes it from the sibling tools, which are all domain-specific lookup utilities like crypto_price and dns_lookup.

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 explicit when-to-use guidance, exclusions, or alternative routing is provided. The sibling tool names imply these are non-LLM tools, and the phrasing 'Ask an LLM a question' gives an implicit usage context, but the description never states when this tool should be chosen over alternatives.

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