get_marketplace_alertsMarketplace alert events (price drops, OOS, MAP breaches, etc.) fired in a window: counts by type and severity, plus the most recent events.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days (default 7). |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the operation as read-only and non-destructive. The description adds value beyond the annotations by specifying the response shape—counts by type and severity plus recent events—which sets expectations for the output granularity and content.
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 a single, information-dense sentence with no filler. It front-loads the core subject and efficiently includes scope, examples, and output characteristics, making every word earn its place.
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-only tool with one optional parameter and a provided output schema, the description covers the domain, temporal context, and result format adequately. No critical usage information is missing, and the tool's behavior is predictable from the annotations and description.
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?
The schema fully documents the single 'days' parameter with range and default, and schema coverage is 100%. The description's 'window' wording aligns with the parameter but doesn't add new semantic details, so the baseline of 3 for high schema coverage applies.
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 tool retrieves marketplace alert events, listing concrete examples (price drops, OOS, MAP breaches) and the output structure (counts by type/severity, most recent events). This distinguishes it from sibling tools like get_notifications or list_qradar_alerts, which target different domains.
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 phrase 'fired in a window' conveys a temporal filtering use case, and the description implies the tool is for summarizing alert activity over a lookback period. It gives clear context for when to use it but does not explicitly name alternatives or exclusions, so it falls just short of a 5.
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.