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

No annotations are provided, so the description carries the full burden. It discloses that the operation is 'non-LLM' and evaluates directly against WorldState, but it does not disclose whether WorldState is mutated, whether the operation is read-only, how parse failures are handled, or what the output/return behavior is.

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, front-loaded sentence with no filler. It is concise and to the point, though somewhat jargon-heavy; the brevity is appropriate given the limited parameter surface.

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?

For a language-grounding tool with no annotations and no parameter documentation, this description is too thin. An agent would still not know expected utterance input, WorldState requirements, output semantics, edge cases, or the intended relationship to related cognitive tools.

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. It gives a hint that 'utterance' is parsed and 'world_state' is the WorldState target, but it does not explain utterance syntax, semantic predicate format, how world_state is structured, or how the optional/null world_state behaves.

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 a specific mechanism: parse an utterance into semantic predicates and evaluate against WorldState, and identifies itself as non-LLM. It is clear enough to distinguish this from more general parse/infer tools, though it does not name any sibling explicitly.

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 phrase 'Non-LLM compositional semantics' implies this is for deterministic, non-generative language grounding rather than an LLM-based approach. However, it never explicitly states when to choose this over siblings like resolve_intent or parse_task, and gives no exclusions or conditions.

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