noun_phrases

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

With no annotations, the description carries the full burden and does disclose useful internals: spaCy statistical chunking for English, a jieba heuristic for Chinese that is candidly labeled as heuristic, and the stanza alternative. It does not cover determinism, performance/model-loading cost, or failure behavior on unsupported languages, which leaves meaningful gaps for a no-annotation 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 core capability and output shape are front-loaded in the first sentence, followed by per-language behavior. The closing line about noun phrases being 'cleaner than raw keyword statistics' is mildly promotional but does earn its place as differentiation.

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 output schema covers the return shape and annotations are absent, but with three completely undocumented parameters the description should have closed the gap on accepted `engine`/`lang` values and the meaning of 'auto'. Language-dependent quality tradeoffs are well covered, so the definition is usable but not fully self-sufficient.

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 description must compensate, and it partially does by explaining the language split and naming three engine implementations (spaCy, jieba, stanza). However, it never states the accepted string values for `engine` or `lang`, nor what the default 'auto' resolves to, so an agent still has to guess the literal values.

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 first sentence gives a specific verb and resource: extract base noun phrases with spans and syntactic roots. It partially differentiates from siblings by contrasting with 'raw keyword statistics' (keyword_extract) and by naming dependency_parse as a higher-quality route, but it does not distinguish itself from pos_tag, tokenize, or constituency_parse, which an agent could plausibly confuse it with.

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?

Usage is implied rather than stated: 'noun phrases are the natural topic units' suggests when to prefer it, and the Chinese guidance explicitly routes to dependency_parse/stanza for higher quality. There is no explicit when-to-use/when-not framing against the many sibling analysis tools, so the agent must infer the choice.

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