---
title: "MCP Workflow Examples for AI Market Research"
description: "[zh] Block-first MCP workflows for cohesive multi-question Studies."
canonical_url: "https://getminds.ai/mcp/zh/workflows"
last_updated: "2026-09-30T13:33:53.886Z"
---

# MCP Workflow Examples for AI Market Research

These examples assume you have connected the [Minds MCP server for ChatGPT, Claude, and Cursor](/mcp/overview). If not, follow the [Minds MCP setup guide](/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:

```text
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
```

模型不能默默批准自己提出的问题或方法。简单 Studies 应保持简单。请检查 `list_research_methods`：Conjoint 和其他可用定量方法会执行已注册的设计与计算；高级方法需明确同意。确认计划前先审阅替代方法。对已关联问题的素材，`study_heatmap` 可读取或启动独立视觉分析。仅报告实际返回的证据。

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.

已确认的运行会像一位受访者填写问卷那样作答：题目按顺序执行，每个 Mind 只能看到自己在本次运行中先前的回答，因此会像真实调查一样出现顺序效应。当研究设计需要控制顺序效应时，请在不同的运行之间轮换题目顺序。如需跳题逻辑，请为题目设置 `askIf`，指定前面的一道选择题及其选项；未被提问的 Minds 不计入该题结果，没有任何人被提问的题目会报告 `not_asked`，而不是失败。运行结束后，`answerConsistency` 会标记与同一 Mind 其他回答相矛盾的回答。这些标记仅供审阅，回答本身永远不会被修改。

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

```text
"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

```text
"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

```text
"Create a Study called 'Sustainable Skincare Launch Study' with both Audiences"
```

### Step 4: Run Survey Questions

```text
"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

```text
"Show me the analytics for the Sustainable Skincare Study"

"Export the Study as a PDF report for the brand team"
```

## 验证受众

要检查受众的回答与已发布的真实调查有多接近，助手会按以下顺序操作：

```text
创建或选择受众（至少 10 个已就绪的 Minds）
  -> validate_audience（查找最匹配的已发布调查，或使用你指定的 benchmarkIds）
  -> 使用 batchId 调用 get_audience_validation，直到 status 为 completed、failed 或 cancelled
  -> 读取综合分数、每项调查的分数和 95% 区间，以及被排除的问题
```

对于直接调用工具的 MCP 客户端，`validate_audience` 和 `get_audience_validation` 可以按名称调用。它们尚未出现在助手的工具发现列表中；同样的功能现在可以通过 v1 API 和受众的验证标签页使用。

一次运行通常需要 10 到 60 分钟，因此助手会定期轮询状态，而不是在一次调用中等待。它会报告每项调查的分数、区间以及调查对象，并向用户展示被排除的问题及其原因。不带 `batchId` 调用 `get_audience_validation` 时，会返回受众的整体效度和本月剩余的套餐内验证次数。

说明部分人群的匹配结果之前，助手必须先读取 `respondentScope`。例如，护士调查可能从包含16个医疗工作者 Mind 的 Audience 中选出12个。展示百分比时应同时注明“16个 Mind 中的12个，护士”，不要将其描述为所有医疗工作者的验证结果。结果提示会区分由群组数据确认的资格、由更广泛个人档案支持的资格、不确定情况以及明确不匹配的情况。

资格判断在收集回答之前完成，并独立于回答内容。个人证据可以补充群组信息的缺口，但汇总分布不能证明某个人符合条件。只有已确认或有证据支持的成员参与，且至少需要10个。未达到门槛时，应解释为什么没有进行调查。模型负责选择对象，匹配率则通过数值比较计算。费用按实际选中的回答者计算，综合分数不包含子集结果。即使部分 Mind 也属于其他 Audience，解释仍应限定于本次 Audience。

示例提示：

```text
"用已发布的调查验证我的 'German SaaS buyers' 受众，并告诉我它有多接近。"
```

## 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](/mcp/agents).

如果缺少某个问题或 Mind 的回答，研究运行可能以 `status: "partial"` 结束收集。此状态为终止状态，请停止轮询并说明缺失情况，不要将结果称为完整。`progress.pct` 衡量已结束的问题数，而非受访者回答覆盖率。新问题输出在 `outputData.responseCoverage` 中保留 `expected`、`received` 以及缺失的 Audience/Mind 成员关系。`progress.partial` 统计已记录覆盖不完整的问题数。旧结果缺少覆盖数据并不证明所有成员都已回答。保留有效的原始回答及其结果，不作修改；另行重试前先检查缺失内容。