Skip to main content
Glama

Minds: Synthetic Market Research Panels

by ai.getminds

Get Study Summary

get_study_summary
Idempotent

Returns or refreshes the semantic summary for a Study as Markdown plus flexible evidence blocks. Website, image, and video analyses retain heatmap-compatible block metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRegenerate even when the covered message range is unchanged.
lengthNostandard
panelIdNoStudy ID (UUID; legacy wire field name: panelId). Omit both panelId and panelName only for the active Study from this MCP session.
refreshNoGenerate or refresh the summary instead of only reading the persisted summary.
studyIdNoStudy ID (UUID). Omit with studyName to use the active Study.
panelNameNoStudy name for fuzzy matching (legacy wire field name: panelName). Omit both panelName and panelId only for the active Study from this MCP session.
studyNameNoStudy name for fuzzy matching. Omit with studyId to use the active Study.

Schema Changelog

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

  1. Added

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already convey idempotency, non-read-only behavior, and non-destructiveness. The description adds that the tool can 'refresh' a summary rather than only reading it, and that output retains heatmap-compatible metadata, but it does not discuss side effects, rate limits, or what regeneration entails.

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?

The description is two sentences with no filler. The primary action and output format are front-loaded, and the second sentence adds relevant, non-redundant detail about metadata retention.

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?

The description gives a solid high-level picture of the return format and refresh behavior, but lacks detail about what 'flexible evidence blocks' means and does not situate the tool among related siblings. Given no output schema and a 7-parameter surface, the description is adequate but leaves meaningful gaps.

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 high at 86%, with detailed parameter descriptions for force, length, refresh, and Study identifiers. The tool description itself adds no parameter-specific semantics, so the baseline score of 3 applies.

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 ('Returns or refreshes') with a clear resource ('semantic summary for a Study') and output format ('Markdown plus flexible evidence blocks'). It is distinct from sibling tools like get_study_run and get_study_status, which target run state and status rather than the semantic summary.

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 gives no explicit guidance about when to use this tool versus alternatives such as get_study_run, get_study_status, or export_study. Some context about active Study selection exists in parameter descriptions, but the tool description itself does not provide usage direction or exclusions.

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