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Dropshipping Pricing & Content Rule Validator

dsers_rules_validate
Read-onlyIdempotent

Validate dropshipping import rules to verify pricing, content, and image configurations against provider capabilities and identify blocking errors before product import.

Instructions

Check and normalize a rules object against the provider's capabilities before importing. Use this to verify pricing, content, and image rules are valid and see exactly which ones will be applied. Returns: effective_rules_snapshot (what will actually be applied), warnings (adjustments made), errors (blocking issues that must be fixed before calling dsers_product_import).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rulesYesRules as a JSON string. Top-level keys: pricing, content, images, variant_overrides, option_edits. Pricing modes: fixed_price (exact dollar amount for all), multiplier (cost × ratio), fixed_markup (cost + dollars). Example: {"pricing": {"mode": "fixed_price", "fixed_price": 9.99}, "content": {"title_prefix": "[US] "}, "images": {"keep_first_n": 5}}
target_storeNoStore ID or display name from dsers_store_discover. Some rule capabilities vary by store.
Behavior4/5

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

While annotations cover safety profile (readOnlyHint, destructiveHint), the description adds crucial behavioral context: it discloses the three-component return structure (effective_rules_snapshot, warnings, errors) and explains what each contains (adjustments made vs blocking issues). The 'normalize' behavior is also disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three efficient segments: action statement, usage context, and structured return documentation. Every clause provides distinct value. No redundant text or tautology.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Compensates well for missing output schema by documenting return values (effective_rules_snapshot, warnings, errors) and their meanings. Establishes clear relationship with sibling tool dsers_product_import. Could mention idempotency explicitly, though covered by annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed parameter descriptions (including JSON structure examples and pricing modes). The description references 'rules object' and rule types (pricing, content, images) aligning with schema but does not add semantic information beyond what the schema already provides. Baseline 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verbs ('Check and normalize') and resource ('rules object') with clear scope ('against provider's capabilities'). Explicitly distinguishes from sibling dsers_product_import by positioning itself as the pre-import validation step.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit workflow guidance: use this 'before importing' and specifically references dsers_product_import as the subsequent step requiring error-free validation. Explains the value proposition ('see exactly which ones will be applied').

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

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