detect_language

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

With no annotations, the description carries the full disclosure burden. It never says whether results are read-only (implied only), how ambiguous or mixed-language input is handled, whether a confidence score is returned, or what the trailing '[conductor: verified stochastic command]' token even means — that artifact is unexplained noise that could mislead an agent.

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 first sentence is tight and front-loaded. The second bracketed fragment adds no semantic value and reads like pipeline metadata accidentally left in the definition.

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 annotations and no output schema, so the return value (ISO 639 code vs. language name, confidence field) is entirely unknown. For a simple single-parameter tool this still leaves the agent guessing at both input constraints and output shape.

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 0% for the single 'text' parameter. The description only restates that text is the input being detected, adding no constraints (length limits, encoding, multi-language handling) that an agent would need to call it correctly.

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?

A specific verb+resource pair ('Detect the natural language') that says exactly what comes out. It is distinguishable from siblings like translate or classify_sentiment, though it never names or excludes them explicitly.

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 when-to-use or when-not-to-use guidance, and no mention of alternatives such as translate (which might be the better call once the language is known). Usage is only implied by the tool name and verb.

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