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Overview

VR3 AI exposes an MCP (Model Context Protocol) server that lets AI assistants like Codex, Claude Code, Claude Desktop, and Cursor access your workspace and documentation. Once connected, an assistant can list your agents, fetch agent definitions, and search VR3 AI docs on your behalf.

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Prerequisites

  • A VR3 AI API key. Generate one at /api-keys (e.g. http://localhost:3010/api-keys for a local deployment). See API Keys.
  • Your VR3 AI MCP endpoint: <YOUR_BACKEND_URL>/api/v1/mcp/ (for example, https://your-vr3-instance/api/v1/mcp/).
The endpoint is also shown in Platform Settings → MCP Server inside the VR3 AI UI.
If you deployed VR3 AI to a remote server using setup_remote.sh, your endpoint is served with a self-signed SSL certificate. Claude Code will refuse MCP connections to it unless you start the CLI with TLS verification disabled:
NODE_TLS_REJECT_UNAUTHORIZED=0 claude
This is only required for self-signed certificates. If you have set up a custom domain with Let’s Encrypt certificates via Certbot, no extra flag is needed.

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Claude Code

Register VR3 AI as an MCP server with the Claude Code CLI: 
claude mcp add --transport http vr3 https://your-vr3-instance/api/v1/mcp/ \
  --header "X-API-Key: YOUR_API_KEY"
Replace YOUR_API_KEY with the key you generated and https://your-vr3-instance with your backend URL. Verify the server is connected: 
claude mcp list

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Codex

Open Codex’s config file (~/.codex/config.toml) and add a vr3 MCP server: 
[mcp_servers.vr3]
url = "https://your-vr3-instance/api/v1/mcp/"
http_headers = { "X-API-Key" = "YOUR_API_KEY" }
Replace YOUR_API_KEY with the key you generated and the URL with your backend MCP endpoint. If you prefer to keep the API key out of config.toml, store it in an environment variable instead: 
[mcp_servers.vr3]
url = "https://your-vr3-instance/api/v1/mcp/"
env_http_headers = { "X-API-Key" = "VR3_API_KEY" }
Then set the API key before starting Codex: 
export VR3_API_KEY="YOUR_API_KEY"
codex
Verify the server is registered: 
codex mcp list
codex mcp get vr3

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Claude Desktop

Open Claude Desktop’s config file (claude_desktop_config.json) and add the vr3 entry under mcpServers: 
{
  "mcpServers": {
    "vr3": {
      "url": "https://your-vr3-instance/api/v1/mcp/",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}
Restart Claude Desktop after saving. The VR3 AI tools should appear in the tool picker.

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Cursor and other MCP clients

Any MCP client that supports Streamable HTTP transport can connect with the same URL and header. Paste the configuration above into your client’s MCP settings file and replace YOUR_API_KEY.

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Example prompts

Once the MCP server is connected, you can drive VR3 AI from your coding agent in plain English. A few prompts to try: Explore your workspace
  • “List my agents in VR3 AI.”
  • “Show me the definition of the agent called Lead Qualifier.”
  • “Which credentials and tools are configured in my VR3 AI workspace?”
  • “List the recordings from my most recent agent.”
Edit an agent
  • “In my Lead Qualifier agent, add a new agent node after the greeting that asks the caller for their budget, then routes to the existing qualification node.”
  • “Add an end-call node to Support Bot that triggers when the user says they are done, with a polite goodbye prompt.”
  • “Rename the intro node in Lead Qualifier to greeting and update any edges that reference it.”
  • “Change the LLM model on all agent nodes in Support Bot to gpt-4o-mini.”
Learn the platform
  • “Search the VR3 AI docs for how to configure a TURN server.”
  • “What node types does VR3 AI support, and what fields does a knowledge_base node take?”
  • “How do I deploy VR3 AI on a custom domain with HTTPS?”
Agent edits are saved as a new draft version — your published agent keeps serving calls until you explicitly publish the draft from the VR3 AI UI.
The API key controls which workspace the assistant sees. Treat it like any other credential — do not commit it to source control or paste it into shared chats.