Skip to main content
Glama

Minds: Synthetic Market Research Panels

by ai.getminds

Get Multi-Question Run Status

get_study_run
Read-onlyIdempotent

Returns durable progress or results for one confirmed multi-question run inside a Study, including its immutable confirmed plan, server-prepared execution plan, respondent-visible question audit, question progress, response artifacts, deterministic method calculations, and stage readiness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdNoMulti-question run ID returned by run_study_questions. Preferred.
panelIdNoLegacy alias for the Study workspace ID. Accepted for compatibility.
studyIdNoStudy workspace ID (UUID). When panelId is supplied for compatibility, studyId may carry the legacy run identifier.
panelNameNoLegacy alias for studyName. Accepted for compatibility.
studyNameNoStudy workspace name for fuzzy matching.

Schema Changelog

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

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds substantial context beyond annotations by detailing the exact contents of the response (immutable plan, execution plan, question audit, progress, artifacts, deterministic calculations, stage readiness). This is especially valuable given there is no output schema.

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 core result is front-loaded in the first clause, and the long enumeration of response components is information-dense rather than redundant. It is a single well-structured sentence, though slightly long due to the itemized list.

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?

With no output schema, the description compensates by enumerating the returned artifacts and noting durability. It also clarifies the run is inside a Study and confirmed, which helps set expectations. It does not mention error cases or how to choose among the five optional identifiers, but the schema covers the identifiers fully.

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%, and each parameter has a clear explanatory description, including which is preferred (runId) and which are legacy aliases. The description adds no new parameter-level meaning; baseline 3 applies when schema carries the full burden.

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?

Description uses a specific verb ('Returns') and a specific resource ('progress or results for one confirmed multi-question run inside a Study'), followed by a precise enumeration of returned components. This clearly distinguishes it from sibling tools like get_study_status and get_study_summary that operate at the study level rather than the run level.

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 phrase 'one confirmed multi-question run' implies this is called after a run has been created, giving clear context. However, it does not explicitly state when not to use it or name alternatives such as get_study_status, so it stops short of full exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Minds: Synthetic Market Research Panels toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources