onto_dl_check

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

With no annotations, the description carries the full burden. It discloses the reasoning method (DL tableaux) and output format (boolean with justification), suggesting a read-only operation. However, it does not explicitly state that it is non-destructive, requires no special permissions, or any potential side effects like loading reasoners.

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 extremely concise with two sentences. The first sentence immediately states the core purpose, and the second clarifies the return value. Every word is functional, and there is no redundancy. It is well-structured and front-loaded.

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 the simplicity of the tool (two parameters, no output schema), the description is complete. It explains what the tool does, the reasoning method, and what it returns (boolean with justification). No additional information is necessary for an AI agent to invoke it correctly.

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 100%, so the baseline is 3. The description text for parameters is identical to the schema descriptions, adding no new meaning beyond what the schema already provides. Thus, the description does not enhance parameter understanding.

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 clearly states the tool's purpose: checking if one class (sub_class) is subsumed by another (super_class) using DL tableaux reasoning. It specifies what it returns (boolean with justification) and the verb 'Check' precisely indicates the action. The sibling tools like onto_reason, onto_diff are distinct, so this tool's niche is well-defined.

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 subsumption checking, but it does not explicitly state when to use this tool over alternatives (e.g., onto_reason for general reasoning) or when not to use it. No usage context or prerequisites are mentioned, leaving some ambiguity for an AI agent.

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