Consumer Research API for AI Agents

Every AI agent stack has tools for search, code, and documents. A consumer research API adds a way to collect evidence from real people when the answer is not already in those sources.

User Intuition connects study planning, participant recruitment, AI-moderated interviews, and cited reports. This guide describes the current contract. Tool calls below illustrate orchestration; they do not represent completed research or fabricated API responses.

The six research tool groups

MCP exposes study planning, recruitment, interviews, results, evidence search, and supporting configuration operations. The CLI provides shell access to research workflows. Use the current tool catalog and API reference for exact names and arguments; tool counts vary by release.

The study workflow connects the research brief to interviews, reports, and source references that the calling agent can inspect and reuse.

Agents can search findings and participant responses across authorized studies, then retrieve the underlying reports and interviews. Results preserve study context and source links; the calling agent interprets the evidence. Search coverage is explicit, and retrieving evidence does not launch research.

Design and approve the study

Create a metadata draft with create_study, then send the research brief through customize_study. Relay any planning questions to the user. Retrieve the persisted plan with get_study and obtain approval before recruitment. The user must choose panel or BYOP explicitly.

These are illustrative MCP tool calls in workflow order, not an unattended launch script. Replace placeholder IDs with values returned by the server. The example assumes the user has chosen panel recruitment.

create_study({"name":"Headline research","recruiting_method":"panel"})
customize_study({"study_id":"<study-id>","message":"Compare our three headline options with US product managers. Explore relevance, clarity, and reasons for preference. <include the options>"})

Continue customize_study with the user’s answers if it returns a planning question. Once a plan is saved:

get_study({"study_id":"<study-id>"})

Show the full saved plan for approval. Use Customize Plan for audience criteria, screeners, and concept or prototype assets; create_study is not a raw discussion-guide submission tool. Voice in English with Elliot is the default unless the user requests other supported settings. Prototype tests support voice or video, not chat.

Estimate and recruit

For a panel study, use launch_panel with dry_run: true to obtain the recruitment estimate. Show the country, language, cost, and timeline; launch with the same settings after approval. Each launch specifies one country. Audiences below 10% incidence require a feasibility request.

launch_panel({"study_id":"<study-id>","target":25,"incident_rate":50,"country_code":"US","dry_run":true})

The target and incidence above are illustrative, not a claim about your audience. Use the approved values. A dry run estimates recruitment; it does not field interviews. After approval, call the same tool with the same settings and dry_run: false. Timing depends on the audience and fielding conditions.

For a BYOP study, use create_participants with 1–100 unique participant emails per batch after the saved plan and invitations are approved. Invitations send by default; set silent: true on individual participant records when invitations should not send. Source customer lists through your own authorized export or integration; MCP has no direct CRM segment-sync tools.

Monitor and retrieve the evidence

Interviews complete over time. Record the study ID so a later session can resume. Provisioning status describes interviewer setup; it does not by itself prove that a panel is fielding or complete.

list_interviews({"study_id":"<study-id>","status":"completed","page":1,"page_size":20})
get_study_report({"study_id":"<study-id>"})

Paginate before counting completions or comparing quality. Study results expose findings, participant responses, sample profiles, recommendations, and source references in JSON. Use generate_report when analysis is needed, and get_interview to verify supporting messages and recording links. Preference shares, credibility scores, and ranked themes are not guaranteed typed fields in this response.

generate_report works on a selected study. If a write times out, inspect the saved study or report before repeating it. A timeout alone does not establish that the operation failed.

For automated notification, the current tools support account-wide completed-interview webhooks. New registrations provide a signing secret. Delivery has no automatic retries, so consumers should reconcile notifications against interview records.

What qualitative data looks like in the API

The report response contains a report string and references, alongside study_id, interview_count, is_stale, and timestamps. References connect report passages to interview evidence and may include recording links. The JSON envelope is structured; the findings themselves are primarily report text.

An agent can use that text to explain why an option appealed to participants and what objections appeared. If your application needs a numerical preference distribution or a typed list of claims, define that extraction in your own application and verify it against the interviews. Include the denominator and missing responses. Do not treat generated interpretation as a platform-provided measurement.

Testing a claim

For a claim such as “Trusted by 10,000 teams,” put the wording, intended audience, and research question into the Customize Plan brief. Ask what people believe it means, what they doubt, and what evidence would make it credible. Review the resulting discussion guide before recruiting.

Use the same study tools for preference checks, claim reactions, and message tests. These are research objectives, not three special API modes with different result schemas.

Reusing past research

Agents can search findings and participant responses across authorized studies, then retrieve the underlying reports and interviews. Results preserve study context and source links; the calling agent interprets the evidence. Search coverage is explicit, and retrieving evidence does not launch research.

Connect your agent

Use the hosted endpoint, https://mcp.userintuition.ai/mcp, with OAuth in compatible clients. For local stdio, run npx -y @userintuition-ai/mcp with USERINTUITION_API_KEY set to your ui_sk_ key. The CLI supports browser login and API-key management. Hosted OAuth does not require exchanging a user-supplied API key.

For a local stdio client that uses an MCP configuration file:

{
  "mcpServers": {
    "userintuition": {
      "command": "npx",
      "args": ["-y", "@userintuition-ai/mcp"],
      "env": {"USERINTUITION_API_KEY": "ui_sk_your_key_here"}
    }
  }
}

Keep real keys out of version control. For shell workflows:

npm install -g @userintuition-ai/mcp
userintuition-mcp login
userintuition-mcp list
userintuition-mcp list_studies

Start with a read such as list_studies to check your connection. See the current MCP setup guide for client-specific instructions and the research skills for workflow guidance.

Pricing

The MCP connection itself has no separate fee. Research uses platform credits, with panel recruitment priced separately in the estimate. Starter credits cost $30; Professional is $2,499/month with 100 credits and $25 additional credits. Chat uses 0.5 credit, voice 1, and video 2 per interview. Three free interviews are available on signup; review recruitment charges before launching a panel study.

See current pricing and agentic research.