An MCP server makes external capabilities discoverable to an AI agent. User Intuition uses that interface for primary customer research: new studies, real participants, interviews, and cited reports.
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.
| Group | Tools | What agents can do |
|---|---|---|
| Studies | 12 | Create and customize drafts, inspect and update metadata, launch or control fielding, delete a study, generate and retrieve its report |
| Participants | 5 | List, create, inspect and update participant records; send a requested reward |
| Interviews | 5 | List and inspect interviews, retrieve usage and participant-grouped records, delete an interview |
| Panel reference and feasibility | 4 | Discover country/language combinations; submit, list and retrieve feasibility requests |
| Webhooks | 2 | Create or delete completed-interview callbacks |
| Configuration catalogs | 5 | Discover languages, modes, study types, individual types and voices |
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.
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_studiesStart 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.
From a question to evidence
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.
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.
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.
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.
Preference testing, claim reactions, and message testing all use this study workflow. The study type and interview format determine how the conversation runs; “Participant Evidence” is a description of the evidence collected, not a separate tool group.
Prompts, skills, and the CLI
The server provides workflow instructions and prompts alongside its tools. The published skills library gives agents guidance on research design, recruitment, monitoring, and source-aware analysis. Skills coordinate tools; they do not add backend capabilities that the tool catalog lacks.
The CLI exposes the same research operations as MCP. It is useful when a shell-oriented agent needs to discover a command, retrieve a study, or incorporate research into a larger script. Check both the returned JSON and the documented command behavior when integrating automation.
Completion and recovery
Studies accumulate interviews over time. Save the study ID and retrieve results in a later session rather than assuming one request completes all fieldwork. Use pagination when counting interviews. Webhooks can notify your application about completed interviews, but their current delivery contract has no retries; use periodic reconciliation for durable workflows.
Pricing
The MCP connection itself has no separate fee. Research uses platform credits, with panel recruitment priced separately in the estimate. User Intuition voice interviews cost $30 each with no subscription required. Recruitment is priced separately. 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.