MCP server

Connect MCP-aware assistants to Cube through a hosted HTTPS endpoint with OAuth and admin-managed defaults.

Cube MCP (Model Context Protocol) lets MCP-compatible AI clients connect to Cube over HTTPS using OAuth.

The MCP server is available on Premium and Enterprise plans.
Users need the Viewer role or higher to interact with the MCP server. Which tools a user sees depends on their role — see Available actions.

What is MCP?

Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to external data sources and tools. The Cube MCP server acts as a bridge between your AI assistant and Cube’s analytics platform, allowing you to ask data questions directly from your coding environment.

Overview

Cube hosts an MCP server endpoint for your tenant. MCP clients connect over HTTPS and authenticate via OAuth.

• Endpoint: https://<cube-mcp-server-host>/api/mcp
• OAuth discovery: https://<cube-mcp-server-host>/.well-known/oauth
• OAuth flow: Authorization Code + PKCE, client_id = cube-mcp-client, scope = mcp-agent-access
• Deployment selection: On connect, the client lands on the tenant default deployment set by your admin (or the first deployment you can access). Clients can also target a specific deployment and agent per request — see Select a deployment and agent.

Admin setup

Prerequisites

Before enabling MCP, make sure you have:

• Admin privileges in your Cube instance
• An active Cube tenant
• MCP server URL configured

1) Confirm MCP server URL

MCP uses your Cube MCP server host. If the URL isn’t configured, the MCP page will show “MCP configuration is unavailable.”

2) Configure deployment access

Go to Admin → MCP Server and use the Deployment Access section to control which deployments MCP clients can reach and where they connect by default:

• Default deployment — the deployment MCP clients connect to unless they explicitly request another one. Choose Automatic (first available) to land each user on the first deployment they can access.
• Limit available deployments — when off (the default), MCP clients can access all deployments the user is permitted to see. Turn it on to restrict MCP to a chosen subset.
• Available deployments — when the limit is on, pick the deployments MCP clients may access. Selecting none disables MCP access for the account.

Deployment access is always intersected with the user’s role-based permissions — clients can only reach deployments the authenticated user is allowed to see.

Connect to Claude

Claude Code

claude mcp add --transport http cube-mcp-server https://<cube-mcp-server-host>/api/mcp

Authentication and usage flow:

1. Run the command copied from Admin → MCP Server → AI Clients → Claude Code.
2. Then run Claude and use /mcp to list available servers.
3. Select cube-mcp-server and choose Authenticate.
4. A browser window opens for authentication.
5. Log into Cube and choose your tenant.
6. Return to Claude Code and start asking questions.

Claude (Team/Enterprise)

1. Open Settings in Claude (web or desktop).
2. Scroll to Integrations and click Add more.
3. Use:
   • Integration name: Cube MCP
   • Integration URL: https://<cube-mcp-server-host>/api/mcp
4. Complete the OAuth flow to grant access.
5. Enable tools in any new chats.

Use Cube in Claude chat

1. Start a new chat in Claude.
2. Open the tools menu and enable Cube MCP (use the tools search if you have many tools).
3. Ask a data question. Toggle the tool off to disable it for that chat.

Claude (Desktop app)

{
  "mcpServers": {
    "cube-mcp-server": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "--transport", "http", "https://<cube-mcp-server-host>/api/mcp"]
    }
  }
}

Connect to Cursor

Add the MCP endpoint under Tools & MCP Settings, then complete the OAuth flow.

{
  "mcpServers": {
    "cube-mcp-server": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "--transport", "http", "https://<cube-mcp-server-host>/api/mcp"]
    }
  }
}

Connect to Codex

Preferred (CLI):

codex mcp add cube-mcp-server --url https://<cube-mcp-server-host>/api/mcp

If this is your first time using MCP in Codex, enable the feature in ~/.codex/config.toml:

[features]
rmcp_client = true

Manual setup:

[features]
rmcp_client = true

[mcp_servers."cube-mcp-server"]
url = "https://<cube-mcp-server-host>/api/mcp"

Then run codex mcp login cube-mcp-server to authenticate.

Connect to other MCP clients

For any MCP-compatible client:

1. Add a new server with the MCP endpoint.
2. The client will auto-discover OAuth and prompt for authentication.
3. Complete the OAuth flow to grant access to your Cube deployment.

Select a deployment and agent

An MCP client is not locked to a single deployment for the whole session. After connecting, it can discover the deployments and agents you can access and target a specific one on each request.

Three tools work together:

• listDeployments — discovery. Returns every deployment you can access via MCP (already filtered by the admin’s deployment-access settings and your permissions) and each deployment’s agents. Use it to find valid deploymentId and agentId values before calling chat. Every deployment offers an Auto agent (agentId: null) in addition to any configured agents.
• chat — accepts two optional selection parameters:
  • deploymentId — the deployment to use for this request. When omitted, the chat uses the deployment from the current session (the default resolved at connect time).
  • agentId — the agent to use for this request. When omitted or null, the deployment’s Auto agent is used. Pass a specific agentId to route to a configured agent.
• loadQueryResults — paginates through the results of a previous query on the same deployment context.

A typical client workflow:

1. Connect

   Complete the OAuth flow. The session is scoped to the tenant default deployment (or the first one you can access).

2. Discover

   Call listDeployments to see the available deploymentId / agentId values.

3. Chat

   Call chat with your input, optionally passing deploymentId and/or agentId to target a specific deployment or agent. Omit both to use the session default deployment with its Auto agent.

Requests are always validated against the admin’s deployment-access settings. A deployment that is outside the allow-list — or that you don’t have permission to see — is rejected with a 403 Forbidden (Deployment <id> is not available via MCP for this account), so neither listDeployments nor the chat selection can reach an excluded deployment.

Available actions

The MCP server exposes 16 tools, grouped below.

Every tool runs as the authenticated user. Queries respect the same permissions as the rest of Cube, including row-level security — MCP is a new way to reach your data, not a new access surface.

Read and write tools

Each tool is annotated as read-only or destructive. MCP clients that honor these annotations — including Claude — run read-only tools automatically and always ask for confirmation before any of the four destructive ones: updateDashboard, publishDashboard, writeDataModelFile, and deleteDataModelFile. Nothing that changes a dashboard or your data model happens without an explicit approval.

Deployments and chat

Tool	Description
listDeployments	Lists the deployments and agents you can reach over MCP.
chat	Asks a question of a Cube agent, optionally targeting a specific deployment and agent.
loadQueryResults	Paginates through the results of a previous query.

See Select a deployment and agent for how these three work together.

Query and discovery

Tool	Description	Access
searchDataModel	Searches the semantic model by similarity — views and their measures and dimensions — to discover what is queryable. Returns compact records (name, title, description, type) to reference in runQuery.	Read-only
runQuery	Runs a Cube SQL query (PostgreSQL dialect) against the SQL API. Returns a schema, a page of rows, and hasMore / totalRows for pagination via offset.	Read-only

Call searchDataModel before runQuery to find exact view and member names rather than guessing them.

Dashboard authoring

These tools build workbooks and dashboards programmatically. Creating and editing workbooks requires the Explorer role or higher.

Tool	Description	Access
readWorkbook	Reads a workbook — its name and its current dashboard draft and published configs.	Read-only
createWorkbook	Creates a new empty workbook, the container that holds reports and a dashboard.	Write
createReport	Saves a query plus its visualization as a report inside a workbook, and returns the reportId a chart widget references.	Write
updateDashboard	Saves the dashboard layout to the workbook draft. Replaces the full widget set and does not go live.	Destructive — prompts
publishDashboard	Publishes the current draft to make it live. Idempotent — republishing an unchanged draft is a no-op.	Destructive — prompts

Drafts are the safety net here: updateDashboard only ever writes to the draft, so a published dashboard keeps serving its previous version until you approve publishDashboard. See Build a dashboard for the full sequence.

Data model editing

These tools read and edit the semantic model source files. They are registered only for users whose role grants permission to edit the semantic model — Admin and Developer by default. Users without it never see them.

Tool	Description	Access
listDataModelFiles	Lists the semantic model source files (raw YAML, JavaScript, or Python).	Read-only
readDataModelFile	Reads one file’s raw source.	Read-only
startDataModelEdit	Enters development mode, starts a dev worker, and returns the dev branchName that every write tool requires.	Write
writeDataModelFile	Creates or overwrites a model source file on the dev branch (whole-file replacement). Recompiles the model and reports valid plus any validationError.	Destructive — prompts
deleteDataModelFile	Deletes a model source file on the dev branch.	Destructive — prompts
getDataModelChanges	Shows the diff of the dev branch against its parent — the pending changes, for review before committing.	Read-only

How model edits stay safe

Letting an AI client edit your semantic model is safe because of four constraints built into the MCP server:

• Edits never touch production. Every write goes to a personal dev branch named dev-<user>-<hash>. The write tools reject any branch that isn’t a dev branch, so the deploy branch is never writable over MCP.
• startDataModelEdit is the only entry point. It returns the dev branchName, and writeDataModelFile and deleteDataModelFile require it. There is no way to write without going through it first.
• Promotion is manual and human. To publish model changes you commit the dev branch from the Cube UI, as described in Development mode. The MCP server deliberately exposes no commit tool — an AI client can prepare changes, but only a person can ship them.
• Registration is permission-gated. The six tools above are only offered to users whose role allows editing the semantic model.

Review pending work with getDataModelChanges before you commit.

Example workflows

Ask a data question

Ask a question in natural language and let the agent do the planning: chat returns the answer along with the SQL it generated, and loadQueryResults pages through large result sets. Use this for summaries, trends, and ad-hoc analysis.

To drive the query yourself instead, call searchDataModel to find the right view and members, then run your own SQL with runQuery.

Build a dashboard

The five dashboard tools are designed to be used in order.

1. Create the workbook

   Call createWorkbook with a name. It returns the workbookId every later step needs. If you were given an existing workbook to build into, call readWorkbook instead and skip to the next step.

2. Create a report per chart

   Call createReport once per chart, KPI tile, or table, passing the workbookId and the SQL query that powers it. Each call returns a reportId.

3. Lay out the dashboard

   Call updateDashboard with the complete widget set, referencing each reportId from the previous step. This replaces the draft layout and saves to the workbook draft — the live dashboard is unchanged. Your client will ask you to confirm.

4. Publish

   Review the draft, then call publishDashboard to make it live. This also prompts for confirmation. It returns the dashboard URL.

To change a dashboard later, call readWorkbook first and edit on top of the current draft rather than overwriting it.

Edit the data model

Call startDataModelEdit to enter development mode and get a dev branchName. Explore the current source with listDataModelFiles and readDataModelFile, then apply changes with writeDataModelFile or deleteDataModelFile, passing that branchName. Each write recompiles the model and reports validation errors, so you can iterate until it compiles. Review the result with getDataModelChanges, then commit the branch from the Cube UI to publish it.

Troubleshooting

• MCP configuration is unavailable: Configure the MCP server URL.
• MCP OAuth integration is not configured: Enable MCP in Admin → Team & Security → OAuth Integrations.
• No deployments available / MCP access is disabled: In Admin → MCP Server → Deployment Access, make sure Limit available deployments is off, or that at least one deployment is selected and the user has permission to access it.
• Deployment <id> is not available via MCP for this account (403): The requested deployment is excluded by the deployment-access allow-list or by the user’s permissions. Call listDeployments to see which deployments are reachable, or adjust the allow-list in Admin → MCP Server → Deployment Access.