get_shelf_availabilityQuick-commerce on-shelf availability (OSA%) over the last 7 days, per channel — the share of observed shelves where the product was in stock. Lower is worse. Requires a Q-Radar subscription.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond annotations: the 7-day time window, per-channel aggregation, value interpretation ('lower is worse'), and the subscription requirement.
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 concise sentences efficiently convey the metric, time range, channel breakdown, interpretation, and access requirement, with no redundant or excessive wording.
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 that there are no parameters, the output schema exists, and read-only annotations are present, the description covers all essential behavioral context including the metric, time window, channel dimension, interpretation, and subscription prerequisite.
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?
This tool has zero parameters, so schema coverage is trivially 100%. The baseline for zero-parameter tools is 4, and no additional parameter detail is necessary or provided.
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 specifies the metric (OSA%), time range (last 7 days), and dimension (per channel), and explains the metric's meaning with an interpretation ('lower is worse'). It distinguishes itself from sibling Q-Radar tools by focusing specifically on shelf availability.
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 for monitoring quick-commerce shelf availability and notes the Q-Radar subscription prerequisite, but it does not explicitly state when to use this tool versus related Q-Radar tools like get_qradar_overview or list_qradar_skus.
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.
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.
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.
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.
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.