get_marketing_summaryCross-platform ad performance for a window: spend, impressions, clicks, conversions, revenue, ROAS, CTR, CPC, with prior-period deltas and a per-platform breakdown (Google/Meta/Amazon Ads, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| range | No | Reporting window: 7d, 28d (default) or 90d. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and non-destructive behavior. The description adds valuable behavioral context by enumerating the exact metrics returned (spend, impressions, clicks, conversions, revenue, ROAS, CTR, CPC), including prior-period deltas and per-platform breakdown. This goes beyond the annotations but does not cover all possible behavioral aspects (e.g., auth, rate 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?
The description is a single, densely informative sentence. It is front-loaded with the core concept ('Cross-platform ad performance'), lists all key metrics, and includes the per-platform breakdown detail without any 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?
Given the tool's simplicity (one optional parameter), full schema coverage, read-only annotations, and an output schema (signaled in context), the description is complete. It specifies the data returned, the breakdown, and the window concept, leaving no critical gaps for an AI agent to invoke it correctly.
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 covers the single parameter 'range' 100%, including its enum values and default. The description's mention of 'window' aligns with this parameter but does not add new semantics beyond the schema's already complete documentation. Baseline of 3 is appropriate.
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 states the tool's function: retrieving cross-platform ad performance metrics (spend, impressions, clicks, etc.) for a specified time window. It is specific and distinguishes this summary tool from analytical siblings like get_analytics_summary or get_dashboard_summary by explicitly naming marketing/ad metrics and platforms.
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 when to use the tool (to get ad performance across platforms) but does not explicitly state alternatives or exclusion criteria. There is no guidance on when not to use it or how it differs from similar summary tools, so the usage context is clear but not fully articulated.
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.