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cdp_list_predictions

Retrieve prediction definitions for a CDP tenant to manage campaign predictions. Use server-side filtering with query parameters for targeted results.

Instructions

List prediction definitions for a tenant (GET /v2/{tenantId}/campaign/predictionDefs). Pass q (e.g. 'isPublished:true') for server-side filtering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tenant_idNo
offsetNo
limitNo
qNo

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 but only partially discloses behavior. It mentions server-side filtering via 'q' and references a GET endpoint, implying a read-only operation, but doesn't cover pagination behavior (offset/limit), authentication needs, rate limits, error conditions, or response format. For a list tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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 efficiently structured in two sentences: the first states the core purpose, and the second adds key usage detail for filtering. Every sentence earns its place with no redundant or vague language, making it appropriately sized and front-loaded.

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 4 parameters), no annotations, and an output schema (which reduces need to describe return values), the description is partially complete. It covers the basic purpose and filtering but misses parameter semantics for most inputs and behavioral details like pagination or auth. It's adequate for a simple list tool but lacks depth for full agent guidance.

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 description must compensate but only adds meaning for 'q' (server-side filtering with examples like 'isPublished:true'). It ignores tenant_id, offset, and limit parameters entirely, leaving their purpose and usage undocumented. With 4 parameters and low coverage, the description provides insufficient semantic context beyond the schema.

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 prediction definitions') and resource ('for a tenant'), which distinguishes it from siblings like cdp_get_prediction (singular retrieval) or cdp_create_prediction (creation). However, it doesn't explicitly differentiate from other list tools like cdp_list_campaigns or cdp_list_audience_defs beyond the resource type.

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

Usage Guidelines3/5

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

The description implies usage for retrieving prediction definitions with optional filtering via the 'q' parameter, but provides no explicit guidance on when to use this tool versus alternatives like cdp_get_prediction (for single definitions) or other list tools. It mentions server-side filtering as a feature but doesn't state prerequisites or exclusions.

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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