ai_text_stats

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

With no annotations, the description carries the full behavioral burden; it does disclose the computed metrics, which is the core behavior, and implies a side-effect-free computation. However it says nothing about input size limits, how reading time is derived (e.g. words-per-minute assumption), or language/markup handling.

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?

A single front-loaded fragment with zero filler; every item in the list conveys a distinct computed metric. It is a sentence fragment rather than a structured statement, but nothing is wasted.

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?

An output schema exists, so return values need no explanation, and the tool is simple with one required parameter. Still, for a metric-computing tool the description leaves loose ends: reading-time methodology and input-size behavior are unstated, which an agent may need to interpret results.

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 description coverage is 0%, so the single `text` parameter has no documentation anywhere. The description implies it is the text to analyze, but adds no detail on format, length constraints, or whether plain text versus markup is expected.

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?

"Text metrics" names the resource and the colon-list enumerates exactly what is computed (chars, words, unique words, sentences, paragraphs, reading time), so an agent knows the tool's output surface. It does not explicitly distinguish itself from text-adjacent siblings like analysis_stats or analysis_frequency, which keeps it from a 5.

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 when-to-use guidance, no mention of alternatives such as analysis_frequency (word frequency) or ai_summarize, and no stated preconditions. Usage is only inferable from the fact that a single text argument exists.

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