text_stats

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

With no annotations, the description carries the burden of behavioral disclosure. It lists the computed outputs and the text input, but does not address edge cases (e.g., empty text), output structure, or side effects. For a pure computation tool, this is adequate but not rich.

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 defining purpose, followed by a minimal argument description. It is front-loaded with the function's goal and avoids redundancy with the schema.

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 one parameter and no output schema. The description covers the operation but omits details about the return format, which an agent would need to interpret the result. This is a notable gap, though the listed metrics hint at the output shape.

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?

The schema only provides the parameter name and type (string). The 'Args' section adds a plain-language explanation ('The text to analyze'), which clarifies the purpose of the single parameter. Since schema coverage is 0%, the description compensates effectively.

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 the specific verb 'Compute' and enumerates exact metrics (character count, word count, sentence count, reading time), making the tool's function unambiguous. It clearly distinguishes from siblings by domain (text analysis vs calculation/search).

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 implies the tool is for analyzing text passages, which is clear enough given unrelated siblings. However, there is no explicit 'when not to use' or mention of alternative tools, so it stops short of full guidance.

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