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Minds: Synthetic Market Research Panels

Export Study Results

export_study
Idempotent

Starts an asynchronous export of Study results and returns an export job ID. Supports executive briefs, full reports, and raw data in PDF, HTML, PNG ZIP, DOCX, PPTX, Markdown, CSV, XLS, or SPSS SAV formats, subject to report-kind compatibility.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoReport kind. Defaults to full_report, except CSV/XLS/SAV which default to raw_data.
forceNoRegenerate instead of returning a cached artifact.
formatNoExport format: "pdf" (default), "html" or "png" for executive summaries, "docx", "pptx", "csv", "xls", "sav", or "md" (also accepts "markdown"). Executive-summary PPTX exports use a slide-native 16:9 layout.
lengthNoRequested report detail for executive_brief and full_report exports.
panelIdNoStudy ID (UUID; legacy wire field name: panelId). Omit both panelId and panelName only when exporting the active Study from this MCP session.
studyIdNoStudy ID (UUID). Omit with studyName to export the active Study.
panelNameNoStudy name (fuzzy matched; legacy wire field name: panelName). Omit both panelName and panelId only when exporting the active Study from this MCP session.
studyNameNoStudy name (fuzzy matched). Omit with studyId to export the active Study.
scheduledRunIdNoExport only the messages produced by one scheduled run of this Study, instead of the whole timeline. Use the run ID from the Study schedule history.

TDQS

A3.6/5.0
Behavior4/5

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

The description adds meaningful behavioral context beyond the annotations: it reveals that the export is asynchronous, returns a job ID, and that format availability is 'subject to report-kind compatibility.' Annotations already mark the operation as idempotent and non-destructive, and the description does not contradict them. It does not mention caching or regeneration behavior, but that is covered in the schema's parameter descriptions.

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?

The description is a single front-loaded sentence that leads with the core async behavior and job ID before listing supported formats. It is efficient and contains no filler, though the phrase 'PNG ZIP' is slightly ambiguous and does not cleanly map to the schema's format enum, which lacks a separate ZIP entry.

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?

For a tool with 9 parameters and no output schema, the description plus the rich schema cover the input side well. However, it omits workflow next steps such as how to check the export job's status or retrieve the resulting artifact, and the compatibility matrix is only vaguely referenced. The description is adequate but leaves gaps that an agent must infer.

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 baseline is 3. The description's mention of report kinds and formats mirrors the schema's parameter descriptions without adding new semantic detail. It does not explain relationships among kind, format, and length beyond the schema, so it adds no significant parameter meaning.

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 opens with a specific verb, resource, and asynchronous behavior: 'Starts an asynchronous export of Study results and returns an export job ID.' This clearly distinguishes export_study from sibling tools like export_audience, export_heatmap, and export_mind by identifying the Study resource. It also enumerates supported report kinds and formats, making the tool's scope unambiguous.

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

Usage Guidelines2/5

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

The description provides no explicit guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. It does not reference sibling tools such as export_audience or get_study_summary, and the important rule about omitting study identifiers only when exporting the active session appears only in the schema, not the description. Usage is only implied by the tool name and purpose.

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.