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List Q-Radar SKUs (worst-OSA first)

list_qradar_skus
Read-only

SKUs in the Q-Radar rotation with current OSA %, risk score, shelf counts, and pricing. Sort by osa_asc to surface the worst-availability SKUs, or filter to those under a target OSA. Own vs competitor SKUs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (1-25, default 15).
citiesNoComma-separated city names to filter.
sortByNoosa_asc = worst availability first (default recent).
osaLensNoOSA basis: consumer (pincode-level, default) or dark-store (shelf-level).
pincodesNoComma-separated pincodes to filter.
ownershipNoDefault all.
platformsNoComma-separated quick-commerce channel codes (e.g. blinkit,zepto,instamart). Omit for all.
minOsaPercentNoOnly SKUs at or below this OSA % (priority view).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare the tool read-only and non-destructive. The description adds behavioral context about sorting behavior and return fields. The title's 'worst-OSA first' conflicts with the schema default (recent), but the description correctly presents osa_asc as an option rather than the default, avoiding a direct annotation contradiction.

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

Conciseness4/5

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

The description is compact, with three sentences that front-load the core purpose. The final fragment 'Own vs competitor SKUs' is slightly cryptic but not wasteful. Every sentence earns its place, though it could be more structured.

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?

With a rich output schema and fully documented parameters, the description provides enough context for an agent to select and invoke the tool. It explains the primary sorting/filtering use case without redundantly restating schema details, making it complete for a listing tool.

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

Parameters4/5

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

Schema coverage is 100% with parameter descriptions. The description adds semantic value by linking osa_asc to 'worst-availability' and minOsaPercent to filtering under target OSA, which goes beyond the raw parameter names and enums.

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

Purpose4/5

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

The description clearly states it lists SKUs from the Q-Radar rotation with OSA%, risk score, shelf counts, and pricing. It differentiates from sibling tools by focusing on SKU-level inventory data, but it doesn't explicitly name alternatives like get_qradar_overview or get_qradar_distribution, so it falls short of a 5.

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

Usage Guidelines4/5

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

The description gives practical usage guidance: use osa_asc to surface worst-availability SKUs, filter by target OSA, and distinguish own vs competitor SKUs. It doesn't state when NOT to use the tool or name a specific alternative, but the context is sufficient for basic selection.

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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TDQS

A3.5/5.0
Disambiguation4/5

Most tools target distinct data points (e.g., get_analytics_summary vs get_brand_score). However, 'search' and 'search_catalog' have overlapping functionality and could cause confusion, and 'fetch' is a helper tied to 'search', adding minor ambiguity.

Naming Consistency4/5

The majority use consistent verb_noun snake_case (e.g., get_inventory, list_stores). Exceptions like 'fetch' and 'search' (without object) break the pattern, but they are few.

Tool Count2/5

43 tools is excessive for a data-retrieval-only API. Many get_* and search_* tools could be consolidated (e.g., search_catalog, search_inventory, search_orders are similar). The large number will overwhelm an agent.

Completeness2/5

The tool set is entirely read-only (get, search, list, fetch). There are no create, update, or delete tools, which is a critical gap for managing e-commerce operations. Agents can only view data, not act on it.

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