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Minds: Synthetic Market Research Panels

Ask One Standalone Audience Question

ask_audience

Submits exactly one respondent-visible standalone question to one existing Audience, identified by exact ID or fuzzy name. It creates a private Study for that Audience, starts asynchronous responses from its Minds, and returns the Study identifier and links. Never use this operation for a questionnaire, battery, section, cohesive question set, or any request with two or more known questions; create a Study and plan the complete multi-question block instead. MCP cannot read or upload a local file:// path. Use a fetchable HTTP(S) URL, a signed URL supplied by the client for the attached file, or an existing Minds workspace upload URL/path. The Study refuses to start if Minds cannot read the asset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional name for the Study created to ask this question. Defaults to the Audience name.
groupIdNoLegacy alias for audienceId. Accepted for compatibility.
questionYesExactly one respondent-visible standalone question for every Mind in the Audience. The system may classify or reformat it, but any text in this field can reach the Minds and influence their answers. Include only the concept, question, and instructions the Minds should receive. Never place planner-only or MCP-client orchestration instructions here.
groupNameNoLegacy alias for audienceName. Accepted for compatibility.
audienceIdNoAudience ID (UUID). Preferred when known.
attachmentsNoFiles/images processed once and given to every Audience member as context. MCP cannot read or upload a local file:// path. Use a fetchable HTTP(S) URL, a signed URL supplied by the client for the attached file, or an existing Minds workspace upload URL/path. The Study refuses to start if Minds cannot read the asset.
audienceNameNoAudience name to resolve via fuzzy match against the user's Audiences.

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses important side effects not visible in annotations: it creates a private Study, starts asynchronous responses from Minds, and returns the Study identifier and links. It also surfaces a critical failure condition: the Study refuses to start if Minds cannot read the attached asset. This goes well beyond the readOnly/idempotent hints.

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 somewhat long but well-structured: purpose first, then behavioral effects, then exclusions, then the file-access constraint. The file:// warning is necessary and reinforces schema details, but the description could be tightened by trimming some repetition of the fetchable URL guidance.

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?

For a tool with 7 parameters and no output schema, the description covers the core workflow: creating a Study, starting async responses, and returning identifiers/links. It could be more complete by mentioning how to follow up on asynchronous results (e.g., status or export tools), but it provides enough to invoke the 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 description coverage is 100%, so the baseline is 3. The description adds some context, such as identifying the audience by exact ID or fuzzy name and clarifying the question content constraints, but most parameter-level meaning is already present in the schema. It does not add substantial new semantics beyond what the schema already documents.

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: "Submits exactly one respondent-visible standalone question to one existing Audience." It also differentiates from siblings by explicitly excluding questionnaires, batteries, sections, or multi-question sets, and by naming the study-creation path as the alternative.

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 the tool: for exactly one standalone question to an existing Audience. It also gives clear when-not-to-use guidance: never for two or more known questions, and instead create a Study and plan the complete multi-question block. This is sufficient routing guidance even without naming the exact sibling tool.

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.