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cdp_list_cube_metadata

Retrieve available OLAP cubes for a CDP tenant, including unique names and captions, to enable data analysis and reporting.

Instructions

List all available OLAP cubes for a tenant. Returns cube unique names and captions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tenant_idNo
offsetNo
limitNo

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 the full burden of behavioral disclosure. It states it's a list operation (implying read-only), but doesn't cover critical aspects like authentication requirements, rate limits, pagination behavior (implied by offset/limit parameters but not explained), error handling, or whether it's idempotent. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 extremely concise—two sentences that directly state the tool's purpose and return values. Every word earns its place with zero redundancy. It's front-loaded with the core functionality, making it easy to parse quickly.

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 the tool's moderate complexity (list operation with pagination parameters), no annotations, and an output schema (which handles return value documentation), the description is minimally adequate. It covers the basic 'what' but lacks context on authentication, pagination usage, error scenarios, and sibling differentiation. The output schema reduces the burden, but behavioral gaps remain significant.

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

Parameters2/5

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

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description mentions 'for a tenant' which loosely relates to tenant_id, but doesn't explain what tenant_id is, its format, or default behavior. It doesn't address offset and limit at all, leaving their purpose (pagination) and usage undocumented. With 3 parameters and no schema help, the description adds minimal semantic value.

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 ('List') and resource ('all available OLAP cubes for a tenant'), specifying what it returns ('cube unique names and captions'). It distinguishes itself from siblings by focusing on cube metadata listing, unlike other list tools for different resources (e.g., cdp_list_audience_defs, cdp_list_dashboards). However, it doesn't explicitly differentiate from other cube-related tools like cdp_get_cube_status, making it slightly less specific than a perfect score.

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. It doesn't mention prerequisites (e.g., authentication), compare it to other cube-related tools (e.g., cdp_get_cube_status for status checks), or specify scenarios where listing cubes is appropriate (e.g., initial discovery vs. detailed retrieval). This leaves the agent with minimal context for tool selection.

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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