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Minds: Synthetic Market Research Panels

Export Audience Brief

export_audience
Idempotent

Exports an Audience brief through the same unified branded renderer used by the web app. Supports Markdown, PDF, DOCX, and PPTX. Binary artifacts are returned as base64.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRegenerate instead of returning a cached artifact.
formatNoExport format: "md" (default), "pdf", "docx", or "pptx"
groupIdNoLegacy alias for audienceId. Accepted for compatibility.
groupNameNoLegacy alias for audienceName. Accepted for compatibility.
audienceIdNoAudience ID (UUID).
audienceNameNoAudience name (fuzzy matched).

TDQS

A4/5.0
Behavior4/5

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

The description adds value beyond the annotations by disclosing that output uses the unified branded renderer, supports specific formats, and returns binary artifacts as base64. This is especially helpful because there is no output schema. It does not contradict the idempotentHint or destructiveHint annotations.

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?

Three sentences, no filler, with the key action and resource front-loaded. The format list and base64 return behavior each earn their place.

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?

Given the rich parameter schema and idempotent annotation, the description covers the essential outcomes: what is exported, supported formats, and return encoding. It lacks explicit prose about cached-artifact behavior or identifier selection, but the force parameter and schema descriptions cover those gaps. Without an output schema, the base64 statement is essential and present.

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 the schema already documents all parameters and aliases. The description adds no parameter-specific meaning beyond naming the export formats, which the schema already enumerates. Baseline 3 is appropriate.

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?

The description uses a specific verb ('Exports') and resource ('an Audience brief'), which clearly distinguishes it from sibling tools like export_study, export_heatmap, and export_mind. The target resource is unambiguous.

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 the tool is for exporting audience briefs, but it does not explicitly state when to use it over the other export siblings or provide any when-not guidance. Sibling differentiation is left to inference from the tool name and the word 'Audience brief'.

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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TDQS

A3.9/5.0
Disambiguation3/5

The tool set has three distinct get_panel_* tools and two draft-saving tools (plan_panel_study, save_study_draft) that could be confused, but the detailed descriptions clarify their specific scopes. Most other tools (list, create, export, ask) target clearly different resources or actions.

Naming Consistency5/5

All tool names follow a consistent verb_noun scheme in snake_case (list_, get_, create_, ask_, export_, plan_, run_, save_). Verb choice maps predictably to the operation, making the set easy to navigate.

Tool Count4/5

At 18 tools, the set is slightly above the ideal 3-15 range but well-scoped for a comprehensive research-panel platform. Each tool addresses a distinct part of the workflow, from group/panel creation to study planning, execution, and export.

Completeness4/5

The surface covers the full research lifecycle: create groups/panels, ask questions, monitor status, export artifacts, and plan/run multi-question studies. It lacks update/delete operations for groups and panels, but these are minor gaps that agents can work around for typical research flows.

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