Mimiq MCP Server

Your AI coding agent tests pages, copy, and flows on simulated users — automatically, while you build. Spots bad copy, confusing UX, and conversion killers before real users do.

Setup (30 seconds)

Claude Code

claude mcp add --transport http mimiq https://mcp.mimiqai.com/mcp

Or add to .mcp.json in your project root:

{
  "mcpServers": {
    "mimiq": {
      "type": "http",
      "url": "https://mcp.mimiqai.com/mcp"
    }
  }
}

Codex

Add to ~/.codex/config.toml (or .codex/config.toml in your project root):

[mcp_servers.mimiq]
url = "https://mcp.mimiqai.com/mcp"

With an API key, set the env var and reference it:

[mcp_servers.mimiq]
url = "https://mcp.mimiqai.com/mcp"
bearer_token_env_var = "MIMIQ_API_KEY"

Then set the env var: export MIMIQ_API_KEY=mq_sk_your_key_here

Cursor

Add to MCP settings (Settings > MCP):

{
  "mimiq": {
    "transport": "streamable_http",
    "url": "https://mcp.mimiqai.com/mcp"
  }
}

VS Code (GitHub Copilot)

Open Command Palette > "MCP: Add Server" or add to .vscode/mcp.json:

{
  "servers": {
    "mimiq": {
      "type": "http",
      "url": "https://mcp.mimiqai.com/mcp"
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "mimiq": {
      "serverUrl": "https://mcp.mimiqai.com/mcp"
    }
  }
}

Claude Desktop

Add to your Claude Desktop config:

{
  "mcpServers": {
    "mimiq": {
      "transport": "streamable_http",
      "url": "https://mcp.mimiqai.com/mcp"
    }
  }
}

No API key needed — you get 100 free personas to start.

Use it

Your agent now has access to Mimiq tools. It uses them proactively — when it builds or changes a page, it tests automatically. No need to ask.

It also works with localhost. The agent starts a temporary cloudflared tunnel, runs the simulation, and tears it down. You build, it tests, in the same flow.

Or ask directly:

"Test my landing page on startup founders"

"A/B test these two headlines on e-commerce shoppers"

"Ask 20 SaaS founders which pricing model they prefer"

Tools

What you get back

Raw per-persona results. Each simulated user has a name, demographics, and independently decides what to do. You get:

• action: what they did (converted, engaged, or bounced)
• monologue: what they were thinking — this is where the real insights are
• objections: specific concerns that stopped them
• what_would_help: what would change their mind
• aggregate counts: how many converted, engaged, or bounced

There are no pre-computed verdicts or scores. Your AI agent analyzes the raw feedback and decides what it means.

When you need more

After 100 free personas, sign

... View full README on GitHub