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Minds: Synthetic Market Research Panels

by ai.getminds

Export Mind Persona Profile

export_mind
Idempotent

Generates a branded profile for one existing Mind, identified by exact ID or fuzzy name. Markdown is returned inline by default; PDF, DOCX, and PPTX artifacts are returned as base64 with a workspace link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRegenerate instead of returning a cached artifact.
formatNoExport format: "md" (default) markdown persona profile (returned inline), "pdf" portrait branded profile, "docx" Word document, "pptx" editable branded deck
mindIdNoMind ID (UUID)
mindNameNoMind name (fuzzy matched)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Added

TDQS

A4.5/5.0
Behavior4/5

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

The description adds behavior beyond annotations by explaining the default return mode (inline Markdown) and the binary artifact behavior (base64 plus workspace link). Since annotations already indicate idempotence and non-destructiveness, this focused extra context is valuable.

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 sentences, tightly written, front-loading the core action and then adding the return format distinction. No filler or redundant repetition of the title.

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?

Given no output schema, the description still provides enough: what is generated, how to identify the target, supported formats, and the return mode for each format. A complete call is derivable without additional assumptions.

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?

The schema covers all 4 parameters with descriptions, so the baseline is solid. The description adds useful context on how formats behave and clarifies that both exact ID and fuzzy name are valid identifiers, which helps an agent select the right operationally.

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 and resource: 'Generates a branded profile for one existing Mind.' It clearly distinguishes itself from sibling export tools (export_group, export_panel, export_heatmap) by naming the target resource and the identifier mechanism.

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?

The description gives clear context: use this when exporting a profile for an existing Mind, identified by exact ID or fuzzy name. It does not explicitly name sibling export tools as alternatives, but the intended scope is unmistakable.

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

A4.1/5.0
Disambiguation4/5

Most tools have a clearly distinct purpose, but ask_audience and ask_study both handle single-question asking and could be confused without careful reading. The get_study_run/status/summary trio also overlaps somewhat, though the descriptions draw useful boundaries.

Naming Consistency5/5

Tool names follow a consistent verb_noun pattern: ask_, create_, export_, get_, list_, plan_, run_, save_. Lists use plural nouns while actions use singular targets, and the naming makes the resource and action predictable.

Tool Count4/5

19 tools is on the heavier side, but the count is justified by the audience/study/draft/export/limits lifecycle. A few tools could arguably be consolidated, but none feel like pure bloat or redundancy.

Completeness4/5

The suite covers the core research workflow well: create audiences, plan and run studies, ask single questions, retrieve results, and export outputs. Missing update/delete operations for audiences and studies are the main gaps, but agents can work around them for typical research tasks.

Resources