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

No annotations exist, so the description is the sole source of behavioral information. It discloses only that text is discarded; it omits whether the result is a plain number, how it handles surrogate pairs or code points, and any error behavior. Given no annotations, this is a minimal disclosure.

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 fragment is admirably short and front-loads the core operation, but it is under-specified rather than structured; it lacks a sentence or usage context. At this size it sacrifices clarity for brevity.

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 tool with ten optional parameters, no required params, no annotations, and no output schema, this description is not enough to invoke correctly. An agent cannot determine what input to supply, what output to expect, or how this relates to sibling counting tools.

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 100%, so each parameter already has documented meaning and the baseline is 3. The tool description adds no parameter-level information and, crucially, never identifies which of the ten optional strings is the 'text' whose length is counted.

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 names a concrete operation: counting UTF-16 units and discarding the input text. It is more informative than a bare name, but it doesn't say which schema value is counted and doesn't contrast with length-related siblings like search-query-len.

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 statement of when to prefer utf16-len over search-query-len, memory-key-count, or other counting tools, and no prerequisites or exclusions. The only contextual hint is 'text discarded', which implies a no-retention use case but is not developed.

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