extract_keywords

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions morphological analysis for keyword extraction but doesn't describe traits like language support, performance characteristics, error handling, or output format. For a tool with no annotations, this is a significant gap in transparency.

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, efficient sentence in Japanese that directly states the tool's function. It's front-loaded with the core action ('形態素解析でキーワードを抽出'), but could be slightly more structured by including key details. There's no wasted text, making it 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 no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of keywords with scores), how errors are handled, or any dependencies. For a tool with 2 parameters and complex text processing, more context is needed to ensure proper 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?

The input schema has 100% description coverage, with clear documentation for 'text' and 'top_n'. The description doesn't add any meaning beyond the schema, such as explaining how 'top_n' interacts with the extraction algorithm or providing examples. Baseline 3 is appropriate since the schema does the heavy lifting.

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 states the tool performs keyword extraction using morphological analysis, which is a clear purpose. However, it doesn't specify what type of keywords (e.g., nouns, named entities) or how it distinguishes from sibling tools like 'analyze_survey' or 'generate_wordcloud' that might also process text. The description is somewhat vague about the exact nature of the extraction.

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 is provided on when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, or exclusions, such as when to prefer 'extract_keywords' over 'analyze_survey' or 'generate_wordcloud'. It lacks explicit usage instructions, leaving the agent to infer based on tool names alone.

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