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

No annotations are provided, so the description carries the full disclosure burden. It does well on outcomes - it names the derived metrics (reading/speaking time, top repeated words) and implies determinism via 'Exactly count' - but says nothing about input size limits, how whitespace/HTML/markdown is handled, or tokenization rules.

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 sentences, zero filler, with the capability list front-loaded and the routing advice ('use this instead of estimating') placed at the end where it functions as a decision cue.

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, so the description usefully enumerates what comes back (counts, reading/speaking time, top words). For a single-input stateless tool this is nearly sufficient; only input limits and text-format handling are left unstated.

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?

One parameter ('text') with 0% schema description coverage, so the description must compensate: it says counts are computed 'in a text', which confirms the parameter's role but not its accepted format or any edge-case behavior. Adequate but thin for the single undocumented input.

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?

Specific verb (count) plus a full enumeration of the resources counted (words, characters with/without spaces, sentences, paragraphs) and the derived outputs (reading time, most repeated words). No sibling tool does anything similar, so no differentiation is required for an agent to select it.

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?

Explicitly instructs 'Use this instead of estimating counts yourself', which gives a clear condition for invoking it over the agent's own arithmetic. It stops short of stating exclusions or limits (e.g. max input length) but the when-to-use signal is unambiguous.

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