text_text_statistics

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

No annotations are provided, so the description must disclose behavioral traits. It only says 'Analyze text' without mentioning side effects, permissions, or whether it modifies data. This is insufficient for an analysis tool.

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 short but includes extraneous information like 'Menu ID: text_statistics' and a pointer to describe_tool. It is front-loaded with purpose but could be more concise.

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?

Given the open schema, lack of output schema, and many sibling tools, the description is insufficient. It does not explain input format, output structure, or how to invoke it properly, relying on describe_tool for completeness.

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 input schema has no named parameters (just additionalProperties: true), and the description does not clarify what parameters are expected (e.g., 'text'). With 100% schema coverage (trivially), baseline is 3, but the description fails to add meaning, leaving ambiguity about how to provide input.

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 clearly states 'Text Statistics' and enumerates specific features: word count, character count, readability scores, reading time, and detailed linguistic analysis. This distinguishes it from simpler sibling tools like text_counter or text_word_frequency by emphasizing comprehensiveness.

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 no guidance on when to use this vs. alternatives. It mentions using describe_tool for full guidance, but does not specify prerequisites, exclusions, or context where this tool is preferred over simpler statistics tools.

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