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DatalisHQ

ZuckerBot

by DatalisHQ
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TypeScript
Remote

zuckerbot_create_lookalike_audience

Create a Meta lookalike audience from a stored seed to reach new people similar to your best customers. Expand a first-party seed into 1%, 3%, or 5% prospecting audience.

Instructions

Create a Meta lookalike audience from a stored seed audience. Expands a first-party seed (e.g., 'customer' stage) into a 1%, 3%, or 5% prospecting audience that Meta will target based on similarity. Typically used for the prospecting tier of an intelligence campaign.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional audience name override
countryNoLookalike country code, such as US or AU
percentageNoLookalike percentage, typically 1, 3, or 5
seed_audience_idYesStored seed audience row ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.4

TDQS

A4.1/5.0
Behavior3/5

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

Describes the core behavior of expanding a seed into a lookalike with typical percentages. No annotations provided, so description carries full burden; however, it omits details like idempotency, error handling, or authentication requirements.

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?

Two concise sentences with no fluff. First sentence states purpose, second adds key details. Front-loaded and efficient.

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?

No output schema; description does not mention return value (e.g., new audience ID). For a creation tool, this is a notable gap. Otherwise covers purpose and typical use adequately.

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

Parameters4/5

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

Schema covers all four parameters with descriptions. Description adds usage context (typical percentages 1, 3, 5) and clarifies that seed_audience_id references a stored row, adding value beyond schema.

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?

Clearly states verb 'create' and resource 'lookalike audience' from a seed. Distinguishes from sibling tools like create_seed_audience and delete_audience by specifying it expands a seed into a prospecting audience.

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?

Provides context that it's for prospecting tier of an intelligence campaign. Implicitly differentiates from deletion or listing tools but lacks explicit when-not-to-use or prerequisites like seed must exist.

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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