data_list_variationsRetrieve product variations (declinaisons) such as size or color. Returns an array of variant definitions with value and optional price delta.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Response format: 'json' (default), 'csv', or 'html' | json |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and description says 'Retrieve', consistent. Description adds return format (array with value and price delta) but no further behavioral details like auth or limits.
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?
Two sentences, front-loaded with purpose, no wasted words.
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?
For a simple read tool with one optional parameter and no output schema, the description adequately explains what is returned. Missing potential details like pagination but acceptable.
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 coverage is 100% with full description for the single parameter. Description does not add additional meaning beyond the schema.
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?
Title 'List item variants' and description clearly state the tool retrieves product variations like size or color. It distinguishes from sibling tools like data_list_products.
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?
Usage is implied by the context of listing variations, but no explicit guidance on when to use this tool vs alternatives or 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.
Add one secure layer between your agents and this server.
Each tool targets a distinct entity or action (account, client, order, product, payment, VAT, etc.), with clear naming like 'account_create' vs 'account_edit' and 'data_list_' prefixed listings. No overlapping purposes detected.
All tool names use snake_case and follow a consistent verb_noun pattern (e.g., account_create, client_add, data_list_clients). Even compound names like auth_login_with_otp adhere to this structure.
41 tools is high but justified by the wide scope of a POS/accounting system (accounts, clients, orders, products, payments, VAT, reports). Slightly above the typical range but well-scoped.
The tool surface covers CRUD for all major entities (clients, departments, products, VAT, payment modes, orders), plus queries, reports, and authentication. No obvious gaps for a small business management server.