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list_symptom_definitions

Read-onlyIdempotent

List VMware Aria symptom definitions to obtain IDs for use in alert definitions. Filter by name or resource kind, with paginated results.

Instructions

[READ] List symptom definitions — use the returned IDs when calling create_alert_definition.

Returns a paginated envelope: items, returned, limit, total (null when the API reports no size), truncated, hint. Check truncated before calling this the complete set.

Args: name_filter: Substring filter on symptom name (case-insensitive). resource_kind: Optional resource kind filter, e.g. VirtualMachine, HostSystem. limit: Max symptom definitions to return (1–500). Default 100. target: Aria target name from config; default when omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
targetNo
name_filterNo
resource_kindNo
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond annotations, such as the paginated envelope format (items, returned, limit, total, truncated, hint) and the explicit warning to check 'truncated' before treating it as a complete set.

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

Conciseness5/5

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

The description is well-structured with a [READ] prefix, a one-line purpose, a concise explanation of the return envelope, and a clear Args section. Every sentence contributes necessary information, especially given the lack of schema descriptions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, usage context, parameter semantics, and return envelope. It is sufficiently complete despite no output schema, and the caveat about the 'truncated' flag ensures the agent understands the data model.

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

Parameters5/5

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

Schema has 0% description coverage, so the description fully compensates by explaining each parameter: name_filter (substring, case-insensitive), resource_kind (e.g., VirtualMachine, HostSystem), limit (1–500, default 100), and target (Aria target name). This is essential and precisely stated.

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

Purpose5/5

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

The description begins with a clear, specific statement: 'List symptom definitions', using a specific verb and resource. It further clarifies the purpose by noting the returned IDs are used when calling create_alert_definition, distinguishing it from sibling tools like list_alert_definitions.

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 provides clear contextual usage: it is a READ operation for fetching symptom definitions to be used with create_alert_definition. This implies when to use it, though it does not explicitly state exclusions or alternative tools.

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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