text_analyse

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

No annotations are available, so the description carries full burden. It does not disclose whether the tool mutates data, requires authentication, or has any side effects. 'Analyse' implies read-only, but this is not explicit.

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 wasted words. It front-loads the key information. Could potentially be structured with an example or output hint, but it is still efficient.

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 one-parameter tool, the description is reasonably complete. It mentions the three metrics returned, but with no output schema, it falls short of fully specifying the return format. Still sufficient for basic usage.

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?

With 0% schema description coverage, the description adds basic meaning for the single 'text' parameter. It clarifies the input is the text to analyze but does not specify length limits, encoding, or formatting expectations.

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?

Description clearly states the tool analyzes text for word count, sentences, and readability. This differentiates it from sibling tools like text_count_occurrences or text_transform. However, it could be more specific about what 'readability' metric is used (e.g., Flesch-Kincaid).

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?

No guidance on when to use this tool versus alternatives like text_count_occurrences or text_transform. No scenarios or exclusions are provided, leaving the agent to infer usage from the name alone.

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