list_coach_recommendationsAI Coach recommendations inbox: detected issues/opportunities with severity and state (open by default), plus AI-generated action plans.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | Filter by state (default open). |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context: it mentions the content (issues/opportunities, severity, state, action plans) and notes that state defaults to open. This goes beyond the annotations without contradicting them.
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 18-word sentence that front-loads the main purpose and efficiently packs in key attributes (severity, state, action plans, default filter). No unnecessary words or repetition.
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 simple schema (1 optional param, no required fields) and the presence of an output schema, the description covers purpose, content, and default behavior sufficiently. It doesn't need to explain return values because output schema exists, though it could hint at pagination or result limits.
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 the 'state' parameter having an enum and its own description. The tool description repeats 'open by default' but does not add new semantics beyond the schema, so baseline 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 it lists AI Coach recommendations, describing them as an 'inbox' of 'detected issues/opportunities' with 'severity and state' and 'AI-generated action plans'. This specific verb+resource+content clearly distinguishes it from sibling tools like get_notifications or list_qradar_alerts.
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 'inbox' and the default state 'open' imply this is the primary view for coach recommendations, giving clear context. However, it does not explicitly mention alternative tools or when-not-to-use cases, so it falls 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.