word_count

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that multiple derived metrics (including a reading-time estimate) are produced, but does not say what rate that estimate assumes, how empty or non-ASCII input is handled, or whether the call can fail.

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?

A single front-loaded sentence listing the computed metrics with zero filler. Nothing redundant and nothing buried.

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 trivial single-input tool with full schema coverage and clear annotations, the description is adequate. With no output schema, however, it only partially conveys the return shape — it lists the metrics but not their keys, units, or whether reading time is in seconds or minutes.

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?

Only one parameter with 100% schema description coverage, so the schema carries the parameter burden and a baseline of 3 applies. The description adds no format constraints or size limits beyond what the schema already states.

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?

Names a specific verb (count) and the exact metrics returned: words, characters, sentences, plus a reading-time estimate. The purpose is unmistakable. It does not, however, distinguish itself from overlapping siblings such as text_analyse, readability_score, or markdown_stats, which likely compute similar text statistics.

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 use this versus the many other text-analysis siblings, no exclusions, and no prerequisites. The agent must infer from the name alone that this is the right pick for simple counts as opposed to readability scoring or frequency analysis.

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