Moonscroll

list_brand_audiences

List audiences with fit scores for a brand. High-fit only unless include_low_fit=true.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "brand_hash": "<brand_hash>"
    },
    "name": "list_brand_audiences"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`brand_hash`	Yes
`include_low_fit`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 2, 2026

TDQS

A3.5/5.0

Behavior3/5

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

With no annotations, the description carries the burden alone. It usefully discloses the non-obvious default that only high-fit audiences are returned unless include_low_fit=true, which is genuine behavioral value. However, it says nothing about pagination, auth requirements, or response shape, leaving significant gaps for an unannotated read tool.

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 short sentences, front-loaded with the core action and followed by the behavior modifier that matters most for correct invocation. No filler.

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?

An output schema exists so return values need no explanation, and the description covers the key filtering default. But with 0% param coverage across 3 parameters and no annotations, the undocumented limit parameter and absent sibling differentiation leave it only minimally complete.

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 coverage is 0%, so the description must compensate. It explains include_low_fit's meaning and default behavior and implies brand_hash's role, but leaves the limit parameter entirely undocumented and gives no format detail for brand_hash. Partial compensation only.

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?

States a specific verb and resource (list audiences) scoped to a brand, plus the distinctive output trait (fit scores). It is distinguishable from the sibling list_audiences by the brand scoping and fit-score emphasis, but the description never explicitly names that sibling to sharpen the contrast.

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 sentence implies usage (get audiences evaluated for a brand) and discloses the default filtering behavior, but gives no explicit when-to-use versus list_audiences or get_audience, and no prerequisites. Usage is inferable rather than stated.

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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