Overview

Regal MCP extends the same AI-assisted workflow pattern that powers Regal Copilot to any MCP-compatible client outside the Regal app. Teams can query Regal data, inspect workflows, and run guided actions from their own AI editors, internal tools, batch scripts, and custom applications — all scoped to their brand and gated by Regal Auth.

MCP (Model Context Protocol) is the open standard that lets AI clients discover and call tools exposed by a server. Regal pairs its MCP tools with Skills — packaged workflow instructions that tell the AI how to use Regal tools for specific goals — so external users get guided workflows, not just raw API access.

When to use Regal MCP

Access

Regal MCP uses OAuth 2.0 authentication via Okta — the same login as your Regal application. Once you add the server config to your AI client, you'll be prompted to log in via browser the first time the server connects. After authenticating, your session token is handled automatically and you don't need to hardcode any credentials.

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SSO users

If your organization uses Okta SSO, additional one-time configuration is required before you can authenticate. See Okta SSO for MCP.

MCP server URL: https://mcp.regal.ai/v1/external-mcp/mcp

Cursor

1. Open Cursor and go to Settings → Tools & MCP → Installed MCP Servers.
2. Click Add MCP Server and paste the config below, or manually add it to .cursor/mcp.json in your project root (project-scoped) or ~/.cursor/mcp.json (global).
3. Fully quit and reopen Cursor — MCP servers only load at startup.
4. On first use, Cursor will open a browser window prompting you to log in to Regal to complete authentication.

{
  "mcpServers": {
    "RegalMCP": {
      "url": "https://mcp.regal.ai/v1/external-mcp/mcp"
    }
  }
}

Note: Cursor has a limit of ~40 active tools across all MCP servers combined.

→ Cursor MCP docs​

Claude Desktop

Option A — Config file

1. Open Claude Desktop and go to Settings → Developer → Edit Config. This opens (or creates) the config file in your default editor.
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
2. Add the Regal server entry inside the mcpServers object and save.

{
  "mcpServers": {
    "RegalMCP": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.regal.ai/v1/external-mcp/mcp"
      ]
    }
  }
}

3. Restart Claude Desktop.
4. On first use, a browser window will open prompting you to log in to Regal.

Option B — Connections UI

1. Go to Settings → Connections → Connectors and click Add.
2. Enter Regal MCP as the name and https://mcp.regal.ai/v1/external-mcp/mcp as the URL, then save.
3. Find Regal MCP under Connections → Connectors — you'll be prompted to log in to Regal to authorize.

→ Claude Desktop MCP docs

Claude Code (CLI)

1. Run this command in your terminal to add Regal MCP:

claude mcp add --transport http --scope user RegalMCP https://mcp.regal.ai/v1/external-mcp/mcp

• --scope user makes the server available across all your Claude Code sessions. Use --scope local (default) to limit it to the current project, or --scope project to share it with your team via .mcp.json.

2. Start a Claude Code session and run /mcp to authenticate:

A browser window will open prompting you to log in to Regal. After completing login, your session token is stored and refreshed automatically.

→ Claude Code MCP docs

Codex (App & CLI)

The Codex desktop app and CLI share the same config file, so this setup covers both.

1. Edit ~/.codex/config.toml (global) or .codex/config.toml in your project root (project-scoped). In the Codex IDE extension, you can open this via the gear menu → MCP settings → Open config.toml.
2. Add the following block:

[mcp_servers.RegalMCP]
url = "https://mcp.regal.ai/v1/external-mcp/mcp"

3. Run the following command to authenticate via OAuth:

codex mcp login RegalMCP

A browser window will open prompting you to log in to Regal. After completing login, Codex handles the session token automatically.

→ Codex MCP docs

ChatGPT

Full MCP connector support requires a ChatGPT Business or Enterprise workspace. Admins must first enable Developer Mode at Workspace Settings → Permissions & Roles → Connected Data.

1. Go to Settings → Connectors → Create.
2. Enter a name (e.g. Regal) and set the server URL to https://mcp.regal.ai/v1/external-mcp/mcp.
3. Save — you'll be prompted to log in to Regal via browser to authorize the connection.

→ ChatGPT MCP connector docs

Tools

All tools are scoped to the authenticated brand — no cross-tenant access is possible. Tools marked with * below currently require a UI session and may return an error on the external MCP endpoint; these are flagged as known gaps.

Read tools

Coming soon — Write tools

These tools are in development and will be available in a future release. They will require explicit human approval at the client level before execution.

Helpful Notes

Recommended starting prompts

These patterns work well with Regal MCP because they give the AI a bounded task, a clear Regal object, and an expected output.

Review AI agent transcripts

"Fetch the last 10 recordings for agent [name] and summarize the most common objections customers raised. Group by theme and flag any calls where the agent deviated from the expected flow."

Debug a task or routing issue

"Look up the task with SID [sid]. Fetch its routing events and tell me where in the routing flow it stalled or failed. Cross-reference the queue and routing rules if available."

Fetch and diff a journey definition

"Fetch journey [name or ID] and its two most recent versions. Summarize what changed between them, focusing on flow logic, node types, and any webhook configurations."

Inspect a contact's history

"Look up the profile for [phone or email]. Fetch their recent events and any active journey executions. Give me a timeline of their last 7 days of activity."

Analyze call quality for a segment

"List recordings from the last 7 days for disposition [value]. Fetch transcripts for up to 50 and identify the most common reasons customers were dissatisfied."

Review agent configuration before a launch

"Fetch the latest version of agent [name]. Summarize its prompt, configured tools, and any active variants. Flag anything that looks incomplete or inconsistent."

General approach

Start specific. Give the AI a named object (agent ID, recording SID, contact phone, journey name) rather than an open-ended question. Regal MCP tools return structured data — the more specific the input, the more useful the output.

Chain tools intentionally. Many useful workflows involve two or three tools in sequence: lookup-profile → fetch-profile-events → list-profile-journey-executions. Describe the end goal and let the AI plan the chain, or specify the steps yourself if you want predictable behavior.

Use Skills for repeatable work. If your team runs the same analysis or review workflow regularly, package it as a Regal Skill so the AI follows the same steps each time instead of improvising.

Keep humans in the loop on writes. Current write tools (when available) only save drafts — they don't publish or go live without a separate action. Still, confirm tool arguments before execution, especially in clients that show a preview before calling.

Watch for the routing rules gap. get-routing-rules and get-routing-rule-versions currently require a UI session and may return an error from the external MCP endpoint. This is a known issue — use list-queues as a workaround for queue-level context in the meantime.