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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions 'non-LLM compositional semantics' and direct evaluation against WorldState, which gives some insight into the mechanism, but it doesn't disclose any limitations, error conditions, or what happens with ambiguous utterances. It's a brief overview without depth.

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 concise sentence that front-loads the core idea. It is efficient with no waste, though slightly terse given the complexity of the tool.

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 appears complex (non-LLM semantics, evaluation against world state) and has no annotations or schema descriptions. The description is too brief to cover what an agent needs to know, such as expected input format, limitations, and how results are returned. The output schema exists, which might clarify return format, but the description doesn't reference it. Overall, incomplete for a semantically complex tool.

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 no parameter help comes from schema descriptions. The description only implies the utterance is parsed and WorldState is used, but doesn't explain the format or semantics of 'utterance' (e.g., natural language syntax) or 'world_state' (e.g., expected structure). The description adds minimal value over the raw parameter names.

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's purpose: parse utterance into semantic predicates and evaluate against WorldState using non-LLM compositional semantics. However, it doesn't differentiate it from the sibling 'cognitive.ground_language' which likely serves a similar purpose, and the term 'ground' is somewhat ambiguous without context.

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 description implies usage for grounding natural language against a world model but provides no explicit guidance on when to use this vs. alternatives like cognitive.parse_task or cognitive.ground_language. No exclusions or context are given.

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