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cdp_get_cube_status_by_names

Check the processing status of specific OLAP cubes by providing their unique names. Use this tool to monitor cube readiness and identify processing issues in Acquia's Customer Data Platform.

Instructions

Get the processing status of specific OLAP cubes by name. Pass a JSON array of cube unique names as a string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cube_namesYes
tenant_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool retrieves status information (implying read-only), but doesn't mention authentication requirements, rate limits, error handling, or what 'processing status' entails (e.g., pending, running, completed). The description is minimal and lacks operational context.

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

Conciseness4/5

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

The description is two concise sentences with no wasted words. The first sentence states the purpose, and the second provides parameter guidance. It's appropriately sized for a simple lookup tool, though it could be more front-loaded with key usage information.

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

Completeness3/5

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

Given that there's an output schema (which handles return values), the description's job is lighter. However, with no annotations, 0% schema coverage, and a mutation-heavy sibling toolset, the description should do more to clarify this read operation's behavior and constraints. It's minimally adequate but leaves gaps in parameter documentation and usage context.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It explains that 'cube_names' should be a 'JSON array of cube unique names as a string', adding crucial format information not in the schema. However, it doesn't mention the optional 'tenant_id' parameter at all, leaving half the parameters undocumented. The value added is partial.

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

Purpose4/5

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

The description clearly states the action ('Get the processing status') and resource ('specific OLAP cubes by name'), making the purpose explicit. It distinguishes from the sibling tool 'cdp_get_cube_status' (which likely has different parameters) by specifying 'by names', though it doesn't explicitly contrast with other cube-related tools in the list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'cdp_get_cube_status' or other status-checking tools. It mentions the parameter format ('JSON array of cube unique names as a string') but gives no context about prerequisites, error conditions, or typical use cases.

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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