distill_learnings

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

No annotations are provided, so the description must fully disclose side effects. It mentions exporting a markdown file and optional rule capture, but it does not state whether it modifies the audit trail, requires authentication, has rate limits, or any other behavioral implications. The lack of annotation coverage leaves significant transparency gaps.

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 sentence that efficiently conveys the main action and key optional behaviors. It is front-loaded with the primary purpose and adds detail without excessive verbosity, though a slightly clearer separation of the alternatives could improve structure.

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?

With 5 parameters, 0 required, and no output schema, the description is incomplete. It does not explain the meaning of brand, category, or min_occurrences, nor does it describe the expected output beyond the markdown export. An agent would struggle to know what values to provide or what to expect in response.

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 only 20% (only capture_rule has a description). The description clarifies capture_rule and export_brain indirectly, but does not explain brand, category, or min_occurrences. Given low schema coverage, the description fails to compensate for the undocumented parameters, leaving agents without sufficient guidance on these inputs.

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 primary action ('Promote recurring audit-trail patterns into brand learnings') and mentions optional rule capture and markdown export. It distinguishes itself from sibling audit tools by focusing on distillation rather than raw log access or detail.

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 when recurring audit-trail patterns exist, but it does not explicitly state when to use this tool versus alternatives like audit_log or audit_get_log. There is no 'instead of' guidance or exclusion of other tools, leaving the decision somewhat inferred.

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