Text Statistics

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists the counts returned but does not define what constitutes a word, character, line, or sentence, nor does it mention edge cases like empty input or how whitespace and punctuation are handled.

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 a single sentence with no filler words. The verb and the object of the operation are front-loaded, and every word contributes to the meaning.

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 simple pure function with one parameter and no output schema, the description covers the core purpose and the types of results returned. It does not specify the exact return shape or edge-case behavior, but these are minor gaps given the low complexity and the clear list of counts.

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 100%, and the schema already describes the only parameter, 'text,' as 'The text to analyze.' The description adds no additional parameter-level meaning beyond what the schema provides, so baseline 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 ('Return') and identifies the exact resource: word, character, line, and sentence counts for a block of text. This clearly distinguishes it from siblings like calculator, slugify, and base64, which perform unrelated operations.

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 explicit statement of when to use this tool over alternatives, but the sibling tools are all clearly distinct utilities, so the intended usage is implied. It does not state exclusions or alternate routes for text analysis, but the context makes the choice nearly unambiguous.

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