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

With no annotations, the description carries the full burden and mostly does: it discloses the model's bias (African-first, traffic-weighted default), a known strength (short text), and the response fields. It omits operational traits such as determinism, latency, or input-size limits.

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 paragraphs: identity and domain first, then parameter and return semantics. The parenthetical qualification is compact and every sentence adds information; nothing is redundant.

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?

There is no output schema, and the description compensates by listing the return fields (ISO 639-3 code, script, name, confidence, runners-up). For a three-parameter utility, the only material omission is the meaning and bounds of 'top_k'.

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%, so the description must compensate. It does well for 'mode', defining both values where the schema has only a bare string with a default, and 'text' is self-evident, but 'top_k' is never mentioned directly — only hinted at via 'runners-up'.

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') with concrete scope ('among 608 languages') and domain character ('built African-first; good on very short text'). Siblings (decide, extract_document, generate_questions, restore_diacritics) are unrelated tasks, so no ambiguity arises.

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?

Gives real decision guidance for the two modes: 'traffic' for user messages and chat versus 'coverage' for building corpora. It does not name alternatives or exclusions, but there is no sibling that overlaps with language detection, so this is clear context rather than a gap.

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