Dropshipping Pricing & Content Rule Validator

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies validation outcomes (warnings for extreme pricing values, errors for blocking issues), security validation (HTML description fields against script injection), and normalization behavior ('normalize a rules object'). No contradictions with annotations exist.

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 efficiently structured with two sentences: the first states the core purpose and usage context, and the second details specific validation behaviors. Every sentence adds essential information without redundancy, and it's front-loaded with the primary function.

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 tool's complexity (validation with normalization and multiple rule types), annotations cover safety aspects well, but there's no output schema. The description compensates by detailing return components (effective_rules_snapshot, warnings, errors) and specific validation logic. However, it could be more complete by explicitly mentioning idempotency or providing examples of error formats.

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%, providing detailed documentation for both parameters. The description adds minimal parameter semantics beyond the schema: it implies the 'rules' parameter is validated against provider capabilities and mentions specific validation thresholds (e.g., multiplier >100x). However, it doesn't significantly enhance understanding of parameter usage beyond what the schema already covers.

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 with specific verbs ('check and normalize', 'verify') and resources ('rules object', 'pricing, content, and image rules'). It distinguishes from siblings like dsers_product_import by focusing on validation rather than execution, and from dsers_product_update_rules by emphasizing pre-import checking.

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 explicitly states when to use this tool ('before importing') and why ('to verify... are valid and see exactly which ones will be applied'). It names a specific alternative (dsers_product_import) for post-validation actions and indicates blocking issues must be fixed before calling that sibling.

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