ai_keywords

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

With no annotations, the description carries the full behavioral burden, and it does disclose one meaningful trait: stopwords for Spanish and English are removed, implying language handling. It says nothing about tokenization, case folding, minimum word length, or ranking ties.

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?

One short sentence, front-loaded with the operation and with zero filler. Its brevity is efficient rather than padded, though it borders on under-specification for a tool with undocumented parameters.

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 and an output schema exists, so return values need not be explained. However, with no annotations and no parameter documentation, an agent still lacks the detail needed to predict the 'top' argument's effect or the stopword language selection behavior.

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 two parameters are undocumented in structured data, and the description does not compensate. 'Top' (count of keywords, default 10) and 'text' are never explained beyond the loose implication of 'top keywords by frequency'.

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 names a specific operation (extract top keywords ranked by frequency) on a specific resource (text), which is clear enough to distinguish it from general frequency counting. It does not explicitly contrast itself with siblings like analysis_frequency or ai_text_stats, so it stops short of 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 guidance on when to use this instead of analysis_frequency, ai_text_stats, or ai_extract, all of which plausibly overlap. The agent must infer the choice from names alone.

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