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

With no annotations provided, the description is the only source for behavioral expectations. It states what is computed but not that the operation is side-effect-free, how edge cases (empty string, Unicode) are handled, or what format the entropy uses. This is acceptable for a pure string utility but does not fully carry the behavioral burden.

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 description is eight words and front-loads the complete set of outputs. Every element is informative, and there is 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?

The tool is simple with a single string parameter and an output schema exists, so an agent can call it correctly from this description. The main gaps are parameter semantics and behavioral caveats, but these are minor for a pure text metrics utility.

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 coverage is 0% and the description never references the 'text' parameter directly. It implies that metrics are computed from the provided string, but it does not clarify counting semantics such as whitespace, newline handling, or Unicode codepoints vs bytes.

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 identifies the tool's function as computing length, word, line, and character counts plus Shannon entropy. This makes the purpose clear and distinguishes it from the sibling encode/decode/convert tools, though there is no explicit verb such as 'computes' or 'returns'.

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 metrics listed imply when the tool is appropriate: whenever an agent needs summary statistics of a string. However, it does not explicitly contrast usage with sibling tools or state when not to use it, so guidance is implicit rather than explicit.

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