netintel_lang_detect

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

With no annotations, the description carries the full burden. It usefully discloses that output includes a confidence score and that 50+ languages are supported, but says nothing about minimum text length, mixed-language behavior, accuracy limits, or auth/cost. Partial behavioral coverage only.

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, front-loaded sentences with no filler. The capability statement leads and the coverage detail follows; nothing is wasted, though it is arguably too terse for a no-annotation tool.

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 one-parameter utility with no output schema, the description covers the return shape superficially (confidence scoring) but leaves behavioral gaps that no annotation or output schema fills. Adequate but not complete for a tool with zero structured support.

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, so the description should compensate. 'Any text' loosely implies the input is free-form string content but adds no format, size, or encoding detail beyond the self-evident parameter name.

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+resource ('Detect the language of any text') and adds scope ('50+ languages') and a return trait ('confidence scoring'). It is distinguishable from text siblings like translate or sentiment, though it does not explicitly name or rule out the nearest alternative (translate/translate_short).

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?

There is no when-to-use guidance, no conditions, and no reference to alternatives despite a large sibling set of text tools (netintel_translate, netintel_classify, netintel_sentiment). An agent must infer the use case entirely from the tool name.

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