Usercall MCP - AI agents that run real user interviews

AI can build products. But it still doesn't talk to users.

Usercall MCP lets AI agents run user interviews via voice calls and return structured insights with themes and verbatim quotes.

Works with Claude Desktop, Cursor, and any MCP-compatible client.

Why this exists

AI agents can now build and ship products extremely quickly.

But most agents still rely on synthetic feedback or assumptions about users.

Usercall MCP lets agents gather real qualitative feedback directly from users.

Example workflow

Agent: "Why are users confused about onboarding?"

→ create_study
→ share interview_link with users
→ get_study_results

The returned interview_link can be shared with participants through email, Slack, Discord, or in-product prompts.

Example result:

{
  "themes": [
    {
      "name": "Onboarding confusion",
      "summary": "Users struggled to understand the second step.",
      "quotes": [
        "I wasn't sure what the app was asking me to do.",
        "I didn't know I had to verify my email before continuing."
      ]
    },
    {
      "name": "Pricing confusion",
      "summary": "Free plan limits were not clearly communicated.",
      "quotes": ["I wasn't sure if the free plan included analytics."]
    }
  ]
}

How it works

AI Agent

↓

Usercall MCP

↓

Usercall Agent API

↓

Real user interviews

↓

Themes and verbatim quotes returned to the agent

Try it in 60 seconds

1. Get an API key

Sign in at app.usercall.co → Home → Developer → Create API key

2. Add to your MCP client

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "usercall": {
      "command": "npx",
      "args": ["-y", "@usercall/mcp"],
      "env": {
        "USERCALL_API_KEY": "your_key_here"
      }
    }
  }
}

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "usercall": {
      "command": "npx",
      "args": ["-y", "@usercall/mcp"],
      "env": {
        "USERCALL_API_KEY": "your_key_here"
      }
    }
  }
}

Restart your MCP client.

3. Ask your agent

Run user interviews to understand why users drop off during onboarding.

Context:
- B2B SaaS product
- 3-step signup flow

Goal:
Identify confusion points and friction.

Target interviews: 5

Show participants this prototype during the interview:
https://www.figma.com/proto/abcd1234/onboarding-flow

The agent will:

1. create a study
2. return an interview link
3. collect responses
4. return themes and verbatim quotes

Structured tool example

Equivalent create_study tool call:

create_study
key_research_goal: "Understand why users drop off during onboarding"
business_context: "B2B SaaS signup flow"
target_interviews: 5
language: "en"

study_media:
  type: "prototype"
  url: "https://www.figma.com/proto/abcd1234/onboarding-flow"
  description: "New onboarding flow concept"

Tools

create_study

Creates an interview study and returns an interview_link to share with participants.

study_media (optional) — visual stimulus shown during all interview questions:

• image: Direct image URL (.png, .jpg, .gif, .webp)
• prototype: Figma prototype URL (converted to interactive embed)
• Media is only visible to web participants; phone callers won't see it

update_study

Updates an existing study. Use this to increase interview slots, add/update media, or disable the link.

The study_media object follows the same schema as in create_study.

get_study_status

Returns the current lifecycle status of a study.

Status values: running · analyzing · complete

Response includes interview progress fields, including completed_interviews and target_interviews.

get_study_results

Returns analysis output once the study is complete.

Summary/full responses include study progress fields and analysis output.

delete_study

Permanently deletes a study and all associated data (recordings, transcripts). Releases unused reserved credits.

Example workflow

1. create_study
   key_research_goal: "Why do users drop off during onboarding?"
   business_context: "B2B SaaS, 3-step signup flow"

   → returns { study_id, interview_link }

2. Share interview_link with participants
   (email, Slack, in-product prompt, etc.)

3. get_study_status
   → "analyzing"

4. get_study_results
   → themes + verbatim quotes returned to the agent

With visual stimulus

1. create_study
   key_research_goal: "Get feedback on new dashboard design"
   business_context: "Redesigning analytics dashboard for power users"
   study_media:
     type: "image"
     url: "https://example.com/dashboard-mockup.png"
     description: "New dashboard design concept"

   → returns { study_id, interview_link }

2. Share interview_link — participants see the mockup during interview

For Figma prototypes, use type: "prototype" with a Figma proto URL.

Requirements

• Node.js 18+
• A valid Usercall API key

Self-hosting / development

pnpm install
pnpm build
USERCALL_API_KEY="your_key_here" pnpm start

Smoke test:

USERCALL_API_KEY="your_key_here" pnpm smoke

Troubleshooting

License

MIT