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

Annotations already establish read-only, idempotent, and open-world behavior. The description adds meaningful operational context beyond that: the Jev engine, an automatic Groq LLM fallback, and a guaranteed under-4.5s response time.

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?

The purpose is front-loaded in the first sentence, with operational details following in a compact second sentence. The sample-endpoint note is short and potentially useful, though slightly extraneous.

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?

With an output schema present, return values need not be explained in detail, and annotations cover the safety profile. The description provides enough context to call the tool correctly; only explicit usage routing is absent.

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 the single 'text' parameter and its 4000-character limit are already fully documented. The description adds no additional parameter syntax or semantic detail beyond the schema, so the baseline of 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 and resource: identify the language of a text. It also specifies the scope (20 common languages or 'other') and the output (confidence score), making it clearly distinguishable from siblings like classify or sentiment.

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?

Usage is implied by the function itself, and the sample endpoint hint suggests a way to try it. However, the description gives no explicit when-to-use, when-not-to-use, or alternative-tool guidance.

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