list_evergreen_queuesRetrieve a brand's Intelligent Evergreen queues, showing activation evidence, cadence, next run time, and current status to track recurring content schedules.
List this brand's Intelligent Evergreen queues and their activation evidence, cadence, next run, and status.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.3.3Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds value beyond that by disclosing what each queue entry carries (activation evidence, cadence, next run, status), which is the key behavioral context for a read tool with no output schema. Only pagination/size limits are unaddressed.
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?
A single front-loaded sentence with no filler. The resource is named first and the returned fields follow compactly, so nothing needs trimming.
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?
With no parameters, no output schema and no nested objects, the description covers the essentials by enumerating the returned fields. It is slightly short of complete because it does not state ordering, pagination, or behavior when no queues exist.
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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for parameterless tools 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?
States a specific verb ('List') and resource ('this brand's Intelligent Evergreen queues'), with scope limited to the current brand. It is distinguishable from singular read siblings like get_evergreen_queue, though it never names them explicitly.
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 listing intent is implied by the verb and brand scoping, so an agent can infer when to call it. However, there is no explicit when-to-use guidance, no exclusions, and no reference to alternatives such as get_evergreen_queue for a single queue or list_evergreen_runs for execution history.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.