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Minds: Synthetic Market Research Panels

Export Website Heatmap

export_heatmap
Idempotent

Exports a completed website heatmap from a Study result, identified by the message ID reported with the completed result. Returns the same ZIP archive as the web app, including its unified-renderer PDF report, Markdown, images, and metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRegenerate the ZIP archive instead of returning the cached artifact.
panelIdNoStudy ID (UUID; legacy wire field name: panelId)
studyIdNoStudy ID (UUID).
messageIdYesID of the completed Study result containing the website heatmap. Completed Study results report this identifier when the answer carries a heatmap.
panelNameNoStudy name (fuzzy matched; legacy wire field name: panelName)
studyNameNoStudy name (fuzzy matched).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish idempotency and non-destructiveness. The description adds useful behavioral context by specifying the exact return artifact: the same ZIP archive as the web app, containing a PDF report, Markdown, images, and metadata. This goes beyond the structured annotations and does not contradict them.

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 tightly written sentences with no filler. The first sentence front-loads the action and key identifier; the second specifies the output contents. Every clause contributes meaningful information.

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 tool with a single required parameter and full schema coverage for optional parameters, the description provides everything needed to call it correctly: what is exported, where the identifier comes from, and what the returned artifact contains. The absence of an output schema is mitigated by the explicit description of the ZIP contents.

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?

The input schema has 100% description coverage for all six parameters, so the description is not required to explain parameter semantics. The description only reinforces messageId's role as the completed-result identifier, adding no information beyond the schema. 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 clearly identifies the resource ('website heatmap') and source ('from a Study result' with 'message ID'). This differentiates it from sibling export tools like export_audience, export_mind, and export_study by resource type.

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 a clear usage condition: export only a completed website heatmap identified by the message ID reported with the completed result. It does not explicitly name alternatives or exclusions, but the resource-specific wording makes the intended context unambiguous.

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.