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SAP Analytics Cloud MCP server

SAP Analytics Cloud is an all-in-one platform combining business intelligence, planning, and predictive analytics in a single cloud solution. With this MCP server, AI agents can create dashboards and stories, build data models, manage planning scenarios, and generate analytics reports through natural language commands.

Setting up an MCP server

This article covers the standard steps for creating an MCP server in AI Gateway and connecting it to an AI client. The steps are the same for every integration — application-specific details (API credentials, OAuth endpoints, and scopes) are covered in the individual application pages.

Before you begin​

You'll need:

  • Access to AI Gateway with permission to create MCP servers
  • API credentials for the application you're connecting (see the relevant application page for what to collect)

Create an MCP server​

Find the API in the catalog​

  1. Sign in to AI Gateway and select MCP Servers from the left navigation.
  2. Select New MCP Server.
  3. Search for the application you want to connect, then select it from the catalog.

Configure the server​

  1. Enter a Name for your server — something descriptive that identifies both the application and its purpose (for example, "Zendesk Support — Prod").
  2. Enter a Description so your team knows what the server is for.
  3. Set the Timeout value. 30 seconds works for most APIs; increase to 60 seconds for APIs that return large payloads.
  4. Toggle Production mode on if this server will be used in a live workflow.
  5. Select Next.

Configure authentication​

Enter the authentication details for the application. This varies by service — see the Authentication section of the relevant application page for the specific credentials, OAuth URLs, and scopes to use.

Configure security​

  1. Set any Rate limits appropriate for your use case and the API's own limits.
  2. Enable Logging if you want AI Gateway to record requests and responses for auditing.
  3. Select Next.

Deploy​

Review the summary, then select Deploy. AI Gateway provisions the server and provides a server URL you'll use when configuring your AI client.

Connect to an AI client​

Once your server is deployed, you'll need to add it to the AI client your team uses. Select your client for setup instructions:

Tips​

  • You can create multiple MCP servers for the same application — for example, a read-only server for reporting agents and a read-write server for automation workflows.
  • If you're unsure which OAuth scopes to request, start with the minimum read-only set and add write scopes only when needed. Most application pages include scope recommendations.
  • You can edit a server's name, description, timeout, and security settings after deployment without redeploying.

Authentication​

SAP Analytics Cloud uses OAuth 2.0 authentication. Set up an OAuth 2.0 client in your SAP Analytics Cloud tenant to obtain credentials.

  • Authorization URL: https://{sac-tenant}/oauth2/authorize
  • Token URL: https://{sac-tenant}/oauth2/token
  • Client ID: Generated from SAC tenant settings
  • Client Secret: Generated from SAC tenant settings
  • Default scopes: Full API access

Replace {sac-tenant} with your actual SAP Analytics Cloud tenant URL.

Available tools​

The SAP Analytics Cloud MCP server exposes data modeling, story creation, visualization management, planning, and data exploration APIs.

ToolPurpose
Story & Dashboard CreationCreate interactive dashboards; add visualizations; design narrative stories; manage layouts
Data ModelsCreate and manage data models; define calculated measures; build hierarchies; configure live connections
VisualizationsBuild charts (bar, line, pie, waterfall); create heat maps; design KPI tiles; customize formatting
Planning & ForecastingCreate plans; configure planning models; run simulations; generate forecast scenarios
Data ExplorationFilter and drill down data; perform ad-hoc analysis; apply multiple dimensions; export results
CollaborationShare stories; add comments and annotations; configure access controls; manage approvals

Tips​

Start with a clear narrative objective for each story and use appropriate visualizations to support insights.

Structure stories logically with sections and include text descriptions to provide context.

Use live connections for real-time data and import static reference data to improve query speed.

Optimize measure definitions to minimize calculation time.

Test model performance with realistic data volumes.

Create separate scenarios for baseline, best case, and worst case planning.

Use version control to track changes and involve stakeholders in scenario reviews.

Document assumptions clearly.

Configure data security at the data model level to restrict sensitive information.

Use model-level permissions to control who can view/modify models.

Regularly audit access logs.

Schedule data model refreshes during off-peak hours and set appropriate refresh frequencies based on business needs.

Monitor refresh performance and configure alerts for failed refreshes.