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1. Choose a connection

Use the hosted Streamable HTTP endpoint with OAuth when your client supports it:
The client discovers OAuth automatically. Sign in and authorize User Intuition when prompted. For a smaller catalog, use https://mcp.userintuition.ai/core/mcp for the 33-tool research workflow, https://mcp.userintuition.ai/read-only/mcp for 25 read-only tools, or https://mcp.userintuition.ai/compact/mcp for 41 tools with grouped administrative operations. Authorize each endpoint separately. The full URL above retains all 50 tools. Local stdio clients can append --profile core, --profile read-only, or --profile compact to the server command. To try study design and reports in staging, connect to https://mcp.sandbox.userintuition.ai/mcp with a staging account. Follow the sandbox quickstart for Test with AI, three synthetic responses, and the participant-link limitation. Local stdio remains supported for clients that need a local process. Create a key under Settings → API Keys at app.userintuition.ai. Keys start with ui_sk_.

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2. Connect your client

In ChatGPT, enable developer mode under Settings → Apps & Connectors → Advanced, then create a connector:
  • MCP Server URL: https://mcp.userintuition.ai/mcp
  • Authentication: OAuth (auto-discovered)
If you previously connected the old User Intuition Human Signal server, remove that custom connector and create it again. Existing connectors can retain obsolete tools after the server is upgraded.

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3. Verify the connection

Ask your client:
List my User Intuition studies.
The agent should call get_account. A successful account-scoped response confirms authentication; merely seeing tool names does not. The current surface contains 50 tools, including queue_customize_study, review_study, search_research, and answer_research, and excluding ask_humans.

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4. Give the agent a workflow

Connection proves the tools work. A Skill tells the agent how to use them reliably. Install the create-study-from-brief Skill, then ask the agent to use it. The Skill adds the question order, readiness checks, plan review, and approval gates needed for a safe study-creation flow.

Install the Skill

Add the reusable study-design workflow to your AI client.

Read the MCP playbook

Understand the workflow rules already advertised by the server.
After installing the Skill, try:
Use the create-study-from-brief Skill to help me design a Panel study about why trial users do not activate. Dry-run the panel cost, but do not launch without my confirmation.
For a healthy end-to-end run, the agent should keep the study name to 40 characters, ask you to choose the recruiting method when it is missing, create a metadata draft, and pass your research brief to queue_customize_study. Poll get_customize_study_job until it succeeds and read its result; use synchronous customize_study for a native concept image. Omitted interview settings default to an English voice interview with Elliot. The default decisions: "human" relays questions[] to you. If you delegate research-design choices, decisions: "agent" lets the planner proceed with assumptions and report them back; remaining required questions still come back in questions[]. The agent must then call get_study, return the complete persisted plan for review, and obtain approval of that exact version before creating BYOP participants or launching paid Panel recruitment. A Panel launch also requires an explicit single country and separate approval of its estimate. Public API MCP tools return full JSON in structuredContent.result; supplemental tools use structuredContent directly. Text content is a short summary. Clients that cannot read structured results can set MCP_FULL_TEXT_RESULTS=true in their local server environment.

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Troubleshooting

If the tools are missing after hosted OAuth setup, restart the client or start a new chat/task. For stdio, restart the client and force a fresh package download with npx -y @userintuition-ai/mcp@latest. See Troubleshooting for authentication and cache checks.