iMario

Create an audience

create_audience

Build a new audience from a sentence describing who they are. Free. Shows what the brief would produce and saves nothing; pass dry_run=false to actually create it.

Returns the definition in three layers: `measured` (filters sized on the measured population),
`estimated` (screening questions; the people who pass are found in the background after the
audience is created, so `ready` is false at first: the audience can be run at once and the
run waits for its first people; incidence is an estimate), and
`unsupported` (criteria that could NOT be applied). Always tell the user what landed in
`unsupported`: the audience does not have that property. Use dry_run=true first when the
brief is ambitious. Nothing is saved until dry_run=false, so show the user what the three
layers say and get their agreement before creating it.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`brief`	Yes	Who, in plain words, in the user's own framing. Include the country. e.g. 'German adults 25-40 who bought from a DTC skincare brand in the last 6 months'
`dry_run`	No	true (default) = show what the brief would produce and save nothing; false = create it and return its id

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No	The new audience's id, once created
`ok`	No	false means the call failed: read `error` and `hint`
`hint`	No	What to change, when ok is false
`name`	No
`next`	No	What to do next
`note`	No	Something to tell the user or keep in mind
`size`	No	Estimated number of people in the country matching the measured filters
`error`	No	Error code, when ok is false
`ready`	No	false: the people matching the estimated criteria are still being found. The audience can already be run; the run waits for its first people.
`country`	No
`dry_run`	No	true: nothing was saved
`share_pct`	No
`size_tier`	No	measured or estimated
`definition`	No	measured, attribute, estimated, unsupported
`provenance`	No	Evidence tier: verified, calibrated or model_est
`ready_in_seconds`	No	Rough wait until the first people exist, when ready is false

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 28, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations only declare the mutation safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false). The description goes far beyond, disclosing that nothing is saved until dry_run=false, that `estimated` layers have ready=false initially and the run waits for first people, and that unsupported criteria must be surfaced to the user. Rich behavioral context.

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 the one-line purpose and the free/no-save default before the layered return explanation. Dense but every sentence carries an operational instruction or state fact; slightly long, but not padded.

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

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with an output schema, the description explains the layered return semantics, the initial ready=false state, and the unsupported-criteria obligation — everything an agent needs to call and report on this tool correctly.

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 100% and both parameters are already fully described in the schema, including the dry_run default and a brief example. The description largely restates this ('pass dry_run=false to actually create it') and adds little semantic detail the schema lacks, so the baseline 3 applies.

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 ('Build a new audience from a sentence describing who they are') and immediately frames it against the read-only siblings (get_audience/list_audiences) by emphasizing creation and the dry-run gate.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('Use dry_run=true first when the brief is ambitious') and prescribes the workflow — show the three layers, get user agreement, then pass dry_run=false. This is genuine when/when-not guidance rather than implied context.

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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