iMario

List audiences

list_audiences

Read-onlyIdempotent

See which audiences this workspace can ask. Free, and the first step of everything else: a study needs an audience id from here.

Each entry says what it is (kind), how big (size, with size_scope and whether the size is
measured or estimated), where its answers come from (provenance: anchored = real respondents'
own answers, verified = sized on measured population data, calibrated = matched to real survey
data, model_est = modelled only), and its definition split into measured / estimated / unsupported
criteria. A locked official audience shows only its unlock card.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Most audiences to return, 1 to 200
`query`	No	Substring to match in the audience name or description
`scope`	No	own = built in this workspace; market = the measured population of each market the workspace holds; official = curated calibrated audiences; all (default)	all
`country`	No	Filter by country name as iMario spells it, e.g. 'United States', 'Germany', 'China'

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No	false means the call failed: read `error` and `hint`
`hint`	No	What to change, when ok is false
`next`	No	What to do next
`note`	No	Something to tell the user or keep in mind
`error`	No	Error code, when ok is false
`audiences`	No	Each: id, name, kind, size, size_scope, provenance, definition {measured, estimated, unsupported}
`markets_held`	No	Countries whose measured populations this workspace may ask

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedOct 6, 2026
- removedInput schema / properties / country / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- removedInput schema / properties / country / default
```
Removed value: -null
```
- addedInput schema / properties / country / type
```
Added value: +"string"
```
- removedInput schema / properties / query / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- removedInput schema / properties / query / default
```
Removed value: -null
```
- addedInput schema / properties / query / type
```
Added value: +"string"
```

Changed2 schema fields changedSep 28, 2026

changedInput schema / properties / scope / description

Previous value: -"own = built in this workspace; market = census populations of markets the workspace holds; official = curated calibrated audiences; all (default)"New value: +"own = built in this workspace; market = the measured population of each market the workspace holds; official = curated calibrated audiences; all (default)"

changedOutput schema / properties / markets_held / description

Previous value: -"Countries whose census populations this workspace may ask"New value: +"Countries whose measured populations this workspace may ask"

First observedSep 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/no open-world, so the safety profile is covered. The description goes further with cost ('Free'), the meaning of each returned attribute (kind, size, size_scope, provenance with four enumerated values), and the edge case that 'a locked official audience shows only its unlock card.'

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?

Front-loads purpose and the workflow role in the first sentence, then explains entry semantics. Dense but well organized; the parenthetical provenance glossary is long but each item carries distinct meaning, so waste is minimal.

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

Completeness4/5

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 not be re-described, and all four parameters are covered by the schema. The description supplies the missing context an agent needs — cost, workflow position, and the locked-audience behavior — though it could say more about cross-workspace scope behavior.

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 100%, so limit, query, scope, and country are fully documented upstream, including the scope enum values and the country spelling convention. The description adds no parameter-level detail, which is acceptable at full coverage but earns only the baseline.

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?

States a specific verb and resource with scope ('which audiences this workspace can ask') and positions itself in the workflow: 'a study needs an audience `id` from here.' This distinguishes it from get_audience (single record) and create_audience without needing the schema.

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?

'Free, and the first step of everything else' gives a clear when-to-use signal and a cost cue, effectively routing the agent here before run_study/estimate_study. It stops short of naming alternatives such as get_audience for retrieval of a single known audience, so it is clear rather than exhaustive.

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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