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

With no annotations, the description carries the full burden and does disclose meaningful traits: execution is entirely local ('no upstream request'), and the supported language set is bounded to ten languages. It still omits input-size limits, behavior for unsupported or mixed-language input, and confidence thresholds.

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 tight sentences with no filler; the core action and the return payload come first, with the language list and local-execution note following.

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?

No output schema exists, but the description compensates by naming the returned fields (code, name, confidence, alternatives). What remains thin is input constraints — how much text to send and what happens with unsupported input.

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 0% and the single parameter has no schema description, so the description must compensate. It only implicitly identifies 'text' as the content to analyze, adding nothing about length limits, encoding, or format.

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 gives a specific verb and resource ('Detect the most likely language of text') and enumerates the returned fields (ISO 639-1 code, name, confidence, alternatives). No sibling tool does language detection, so it is trivially distinguishable from the listed alternatives.

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 only implied: the agent can infer you call this with a block of text to identify its language. There are no explicit when-to-use/when-not conditions and no alternatives named, though the sibling set contains no competing detector to route to.

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