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Minds: Synthetic Market Research Panels

List Study Drafts

list_study_drafts
Read-onlyIdempotent

Lists durable unfinished study drafts, or returns the complete saved planning state for one exact draft ID. Draft records are distinct from running or completed studies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draftIdNoExact study draft ID to retrieve. Omit to list all resumable study drafts owned by the authenticated user.

Schema Changelog

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

  1. Changed2 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / draftId / pattern
      Added value: +"^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$"
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description builds on that by adding that it covers durable unfinished drafts and returns the complete saved planning state for an exact ID. No contradiction is present, and the additional context is useful for invocation.

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 tight sentences deliver the core function and key distinction with no filler. The most important usage distinction is front-loaded before the clarifying sentence about draft records.

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 a single optional parameter and read-only annotations, the description covers what it lists, what it retrieves by ID, and how drafts relate to other study statuses. The absence of an output schema means return format details are not fully specified, but the description gives enough context for effective selection and use.

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 already fully documents the optional draftId with 100% coverage, so the baseline is 3. The description adds meaningful operational meaning beyond the schema by linking draftId omission to listing and draftId presence to returning saved planning state.

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 names a precise verb and resource: listing durable unfinished study drafts or retrieving a complete saved planning state for one draft ID. It also explicitly distinguishes draft records from running or completed studies, which differentiates the tool from planning/run siblings.

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 by describing both operating modes and noting that drafts are separate from running or completed studies. It implies when to use this tool for resumable drafts rather than active studies, though it does not explicitly name alternative sibling tools or provide direct when-not-to-use guidance.

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
Disambiguation5/5

Each tool has a clearly distinct purpose, from audience creation and study management to question planning/execution and exports. Even the similar ask_audience and ask_study are unambiguously differentiated by scope (audience-level vs study-level), and the extensive descriptions reinforce the boundaries.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., list_audiences, create_study, export_study). There are no mixed conventions or vague verbs, making the naming highly predictable.

Tool Count4/5

With 19 tools, the count is at the upper end of reasonable for a complex domain like market research panels, but each tool serves a distinct function. The set is not bloated; it reflects the multifaceted nature of managing audiences, studies, questions, and exports.

Completeness3/5

The surface covers the core research lifecycle—create audience/study, plan and run questions, monitor status, and export results. However, it lacks update and delete operations for audiences and studies, which are typical CRUD gaps that agents may encounter. The workflow is otherwise complete.