Analyze text statistics

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds value by disclosing the analytical scope, including derived metrics like average lengths and estimated reading time, which are not implied by the annotations alone. There is no contradiction.

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 carry all essential information: what is computed and when to use it. The metric list is front-loaded and every clause earns its place with no filler.

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?

With one simple parameter, a full output schema, and strong annotations, the description is complete for an agent to invoke the tool correctly. It covers purpose, use cases, and behavioral scope without needing to explain return values.

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%, and the parameter description 'The text to analyze' is already clear. The tool description adds no new parameter-level semantics, such as formatting constraints or edge-case behavior, so the baseline of 3 is appropriate.

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 uses a specific verb ('Count') and names the exact resource ('a piece of text') plus a rich list of concrete metrics: characters, words, unique words, sentences, paragraphs, lines, and reading time. This clearly distinguishes it from siblings like summarize_text, extract_keywords, or convert_case.

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?

The description provides clear use cases: 'check length limits or profile a draft before editing.' It does not explicitly name sibling alternatives or state when not to use it, but the context is strong enough for an agent to select it appropriately.

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