---
title: "MCP Workflow Examples for AI Market Research | Minds"
canonical_url: "https://getminds.ai/mcp/workflows"
last_updated: "2026-09-19T22:00:41.215Z"
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  description: "Workflow examples for using the Minds MCP server to create Audiences, run cohesive multi-question Studies, test concepts, and export reports."
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  "og:title": "MCP Workflow Examples for AI Market Research | Minds"
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  "twitter:title": "MCP Workflow Examples for AI Market Research | Minds"
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Minds

Minds Team # **MCP Workflow Examples for AI Market Research** Workflow examples for using the Minds MCP server to create Audiences, run cohesive multi-question Studies, test concepts, and export reports. These examples assume you have connected the [Minds MCP server for ChatGPT, Claude, and Cursor](https://getminds.ai/mcp/overview). If not, follow the [Minds MCP setup guide](https://getminds.ai/mcp/setup) first. ## Guided Study Confirmation Workflow For a broader task, multiple questions, a visual asset, structured outputs, or an explicit research method, the assistant follows this sequence:```
User request
  -> plan_study_questions (draft only)
  -> present captured intent, main source, questions, methods, and outputs
  -> ask every returned confirmation question
  -> plan_study_questions again if the user answers or changes anything
  -> explicit user confirmation of the exact revision
  -> run_study_questions
  -> get_study_run until collection settles
  -> get_study_summary
``` The model must not silently approve its own questions or method. Keep simple Studies simple. Check `list_research_methods`: Conjoint and the other available quantitative methods execute their registered designs and calculations; advanced methods require explicit opt-in. Review any proposed fallback before confirming the plan. For an asset already attached to a Study question, `study_heatmap` reads or starts its separate visual analysis. Report the evidence actually returned. The original `plan_study_questions.request` is planner input and is not sent verbatim to Minds. The preview labels the exact proposed respondent-visible question text; after confirmation, `get_study_run` exposes the confirmed or server-prepared question set as a visibility audit. By contrast, treat all text in a direct `ask_study.question` as respondent-visible input: the system may classify or reformat it, but any part can reach the Minds and influence their answers. Keep planner and MCP-client orchestration notes outside direct questions. At the execution boundary, a `plan_limited` response means nothing was started: explain the Study-answer limit and required upgrade. During polling, `status: plan_limited` means the processor preserved partial answers but stopped the remaining questions at the paywall. Never call either case complete, and never hide it behind a generic error. After an upgrade, use a follow-up run for only the unanswered questions. This lifecycle plans research, not every Study action. A standalone “export this” request routes to `export_study`. “Show the same result differently” reads the existing Study or summary and changes presentation without queueing new respondents. Only create a new plan when the requested output changes what evidence the Study must collect. ## Consumer Research Workflow A typical workflow for a brand team researching a product launch: ### Step 1: Create Consumer Personas```
"Create five consumer personas for our skincare launch:
 - 'Lena, 22, Berlin' — Gen Z student, eco-conscious, discovers brands on TikTok
 - 'Maya, 19, London' — retail worker, trend-driven, shops based on peer recommendations
 - 'Aisha, 24, NYC' — junior designer, budget-conscious, cross-references Reddit reviews
 - 'Sarah, 35, Munich' — part-time teacher, mom of two, safety-first buyer
 - 'Jessica, 38, Chicago' — marketing manager, mom, trusts dermatologists over influencers"
```### Step 2: Organize into Demographic Audiences```
"Create an Audience called 'Gen Z Women (18-25)' with Lena, Maya, and Aisha.
Create another Audience called 'Millennial Moms (30-42)' with Sarah and Jessica."
```### Step 3: Create a Research Study```
"Create a Study called 'Sustainable Skincare Launch Study' with both Audiences"
```### Step 4: Run Survey Questions```
"Plan one launch-research question block for the Study with these sections:
1. Price sensitivity: On a scale of 1-10, how likely are you to switch to a sustainable skincare brand if it costs 20% more?
2. Discovery: Where do you typically discover new skincare products?
3. Switching barriers: What would make you stop buying from your current skincare brand?
Show me the complete plan for confirmation, then run all three questions together."
```### Step 5: Analyze & Export```
"Show me the analytics for the Sustainable Skincare Study"

"Export the Study as a PDF report for the brand team"
```## Use Cases ### Product Concept Testing Create Studies with target consumer Audiences to test product concepts, packaging, and naming before investing in production. Compare reactions across Audiences instantly. ### Pricing Research Survey synthetic consumers on willingness to pay at different price points. Identify the sweet spot where value perception meets margin targets. ### Brand Perception Audit Build Studies containing your target Audiences. Ask about brand awareness, trust, and purchase intent in one cohesive question block. Compare your brand against competitors through consumer eyes. ### Campaign Message Testing Test ad copy, taglines, and visual concepts with synthetic target audiences before committing media spend. Identify which messages resonate with which segments. ### Market Entry Research Entering a new market? Create consumer personas for the target region and test product-market fit, cultural sensitivities, and channel preferences. ## Example Conversations ### Quick Consumer Insight**You:** "I need to understand how Gen Z and millennials in Europe feel about subscription-based skincare. Create one Audience for each segment and plan one cohesive Study asking what would convince them to subscribe."**AI Assistant:** Drafts the Minds and audience structure, creates a Study, presents the exact proposed research question(s), and requests confirmation when the request expands into a broader Study. After execution, it reports the actual returned grouped responses without pre-writing a conclusion. ### Expert Consultation**You:** "Talk to my Brand Strategy expert about positioning a premium organic baby care line in the German market."**AI Assistant:** Uses `chat_with_mind` to query the expert Mind and presents its actual response, preserving any returned citations. It labels the output as synthetic expert perspective rather than independent legal or regulatory advice. ### Competitive Analysis**You:** "Create a Study with 'Loyal Customers' Audiences for our top 3 competitors. Plan one block asking what they love most about their current brand and what frustrates them."**AI Assistant:** Creates private synthetic Audiences, confirms the comparative multi-question research plan, runs the Study once, and reports only differences supported by the returned responses and calculations. ## Automation guardrails - Keep new Audiences and Studies private unless the user explicitly requests a public link. - Planning does not start research. Confirm the exact latest revision before `run_study_questions`. - Poll status tools instead of treating a timeout or elapsed duration as completion. - Require explicit confirmation immediately before lifecycle deletion actions. - Preserve citations, resource IDs, workspace/shared links, and download links exactly. - Treat `plan_limited` before execution as “nothing started” and `status: plan_limited` during execution as partial, preserved work. - Use `list_research_methods` before promising a named method; only `executable: true` is runnable. - Do not infer customer findings in advance. The example prompts in this guide describe workflows, not guaranteed conclusions. For a full routing and safety contract, see the [MCP operating guide for agents](https://getminds.ai/mcp/agents). When `get_study_run` returns `partial`, give the user an incomplete-results handoff instead of continuing the polling loop. For example, four settled questions can still include one question answered by only nine of ten Minds. Explain that difference before summarizing: `progress.pct` tracks question settlement, while `progress.partial` identifies how many questions have recorded response gaps. Inspect each artifact's `outputData.responseCoverage` for the expected and received counts and missing Audience/Mind memberships. Historical artifacts without these fields cannot establish full participation. Preserve the answers already collected, identify what is absent, and agree on a separate retry rather than silently replacing the original results. [Minds](https://getminds.ai/)© 2026 Minds. Your target audience. AI-driven and grounded in transparent evidence. Build within minutes. 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