discover_powl

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

No annotations provided. The description discloses the return value structure (handle, root operator name, child count) and mentions the variant selection default, but does not discuss side effects, permissions, or other behavioral traits.

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 relatively concise with two paragraphs covering purpose and return details. Slightly longer than necessary but generally efficient.

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 single parameter, the description explains the output schema but fails to clarify what `log_id` represents. The mention of a non-existent `variant` parameter detracts from completeness.

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%. The description does not explain the only parameter `log_id`, and introduces `variant` which is not in the input schema, causing confusion. No semantic value added for parameters.

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 identifies the tool as discovering a POWL model and explains its specific use case for partial-order dependencies, distinguishing it from other process discovery tools like discover_process_tree.

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 states 'Useful when the discovered model has unclear sibling ordering,' providing a clear when-to-use condition. However, it does not explicitly exclude alternatives or discuss when not to use it.

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