CRM Analytics MCP Server (Beta)

The crma-beta server gives AI agents direct, read-only access to CRM Analytics without any custom integration. With a single OAuth connection, agents like Claude, Cursor, ChatGPT, and Agentforce Vibes 2.0 can discover your analytics apps, inspect dataset schemas, run Salesforce Analytics Query Language (SAQL) queries, and read dashboard and lens metadata.

When an agent queries through this server, it first inspects a dataset’s schema through its extended metadata (XMD) to generate accurate SAQL queries with the correct syntax and field API names. Because the server handles schema resolution automatically, users get accurate results without needing to know SAQL syntax or field API names themselves. For details on how dataset structure and formatting are defined, see the Salesforce Analytics Extended Metadata (XMD) Developer Guide.

Using CRM Analytics MCP provides several key benefits:

• Natural-Language Data Access: Business users can ask questions like “What were our top 10 accounts by revenue last quarter?” and get formatted results, without writing SAQL or knowing the underlying dataset schema.

• Automated Analytics Workflows: Developers building internal reporting tools can pull dataset schemas and query results programmatically, without hard-coding SAQL or managing authentication themselves.

• Complete Asset Visibility for Admins: Admins can inventory every app, dataset, and dashboard across the org in minutes, building a complete analytics catalog for governance and auditing.

Common questions about CRM Analytics MCP server and the Model Context Protocol.

The Model Context Protocol (MCP) is an open standard that enables AI models to securely access data, tools, and context from specialized systems. CRM Analytics MCP server uses the protocol to expose CRM Analytics datasets, folders, dashboards, and lenses to external AI agents.

Yes. Every request is enforced against the connecting user’s own Salesforce permissions: field-level security, object-level security, row-level security, and sharing rules. Your agent can never see data or assets that you can’t see in CRM Analytics directly. Authentication uses individual OAuth 2.0 and PKCE. Shared service account tokens aren’t supported.

Your agent doesn’t write SAQL blind. The typical workflow is:

• list_datasets → get_dataset - to capture the dataset’s currentVersionId
• get_xmd - to get exact field API names, types, and labels
• execute_query - to run the generated SAQL and return structured results

Because the agent inspects the real schema before generating a query, it produces syntactically correct SAQL without you needing to know the dataset’s field names in advance.

Yes, the CRM Analytics MCP server is available in production, as well as on sandbox and scratch orgs.

To use it, you also need a CRM Analytics license, System Admin permissions to create the external client app and enable the server, and a supported MCP Client. Supported clients include Agentforce Vibes 2.0, Claude (Desktop, Web, Code), Cursor, ChatGPT developer mode, or any client supporting OAuth 2.0 and PKCE.

All CRM Analytics tools are currently read-only.

Use these server URLs to access the production and sandbox versions of the MCP server.

• Production: https://api.salesforce.com/platform/mcp/v1/analytics/crma-beta
• Sandbox/Scratch: https://api.salesforce.com/platform/mcp/v1/sandbox/analytics/crma-beta

• “What analytics apps do I have access to?”
• “What CRM Analytics datasets do I have access to right now?”
• “Give me the full metadata for the Executive Pipeline dataset.”
• “What are the exact fields, dimensions, and measures in the Opportunity dataset?”
• “What were our top 10 accounts by revenue last fiscal year?”
• “What was the total pipeline by region last quarter?”
• “Show me rep performance versus quota this month.”
• “Show me all the dashboards in my Sales Application folder.”
• “List all the saved explorations I’m permitted to view.”

The CRM Analytics MCP server provides a suite of read-only tools that allow external AI agents to interact with your CRM Analytics assets. After you complete the initial setup, you no longer need to manually select or configure these tools. Ask your preferred MCP-compliant AI client, such as ChatGPT, Claude, or Cursor, questions using natural language, and it automatically selects the right tools for the task.

For example, when you ask, “What were our top 10 accounts by revenue last quarter?”, the AI agent calls list_datasets, get_dataset, and get_xmd to understand the data structure, generates valid SAQL, and executes it via execute_query.

The available tools are grouped by their functional role within an analytical workflow.

External MCP-compliant AI agents use these tools to navigate CRM Analytics folders before scoping deeper searches. Folders represent analytics apps.

Returns all analytics apps that the connected user can access. Analytics apps or folders are containers for related dashboards, datasets, and lenses. Use the returned ID to scope other list_ calls.

Inputs:

Outputs: A collection of folder objects, each with ID, label, description, template source, and permissions.

Returns full metadata for a single analytics app by ID, including its label, description, template source, and permissions.

Inputs:

Outputs: A folder object with full metadata including label, description, template source, and permissions.

External MCP-compliant AI agents use these tools to discover datasets and inspect their schemas before building queries.

Returns datasets available in a specific app or across the org, with names, IDs, and last-modified dates.

Inputs:

Outputs: A collection of dataset objects with names, types, and last-modified dates.

Returns full metadata for a dataset, including field names, data types, and labels for dimensions and measures. Use this to capture the dataset’s currentVersionId, which get_xmd and execute_query require.

Inputs:

Outputs: Full dataset metadata including field names, data types, dimension and measure labels, and current version information.

Returns the extended metadata (XMD) field schema for a dataset version, including exact field API names, data types, labels, and formatting. Use this after get_dataset to get the precise field names an agent needs to build valid SAQL.

Inputs:

Outputs: Extended metadata for the dataset version, including field API names, data types, labels, and display formatting.

External MCP-compliant AI agents use this tool to execute analytical queries once they understand the dataset schema.

Executes a SAQL query against a CRM Analytics dataset and returns structured results. The AI agent generates SAQL automatically from the natural-language request, using the schema returned by get_dataset and get_xmd.

Inputs:

Outputs: Structured JSON query results containing rows, column metadata, and query execution details.

External MCP-compliant AI agents use these tools to discover and inspect prebuilt dashboards and saved explorations (lenses).

Returns all CRM Analytics dashboards in a specified app or across the org, with names, IDs, and app context.

Inputs:

Outputs: A collection of dashboard objects with names, IDs, types, and app context.

Returns full metadata for a single dashboard, including widgets, steps, and filter configurations.

Inputs:

Outputs: Full dashboard metadata including widgets, steps, filter configurations, and layout details.

Returns all saved explorations (lenses) that the connected user can access, with names, IDs, and the datasets they reference.

Inputs:

Outputs: A collection of lens objects with names, IDs, and the datasets they reference.

Returns full metadata for a single lens, including the query definition and dataset reference.

Inputs:

Outputs: Full lens metadata including the query definition, visualization configuration, and dataset reference.

Use these solutions to resolve common issues and optimize the performance of CRM Analytics MCP server across different AI clients.

Server Connection Fails: Verify you’ve created an external client app using OAuth 2.0 and PKCE, and that an administrator has enabled CRM Analytics MCP server in Setup (MCP Servers under API Catalog). Both steps are required before any client can connect.

Multitenant Conflicts: Because the MCP specification doesn’t support multitenant systems like Salesforce, log out of all other Salesforce orgs, log into the target org in your default browser, and keep that browser window open before connecting your client. The client opens a new tab in that same window during authentication. Most connection failures trace back to skipping this step.

Describe Your Goal, Not the Tool: Say “Show me open opportunities by stage” rather than “call list_datasets.” The AI agent handles tool selection.

Improve Query Accuracy with Field Descriptions: Adding field labels and descriptions to your dataset XMD helps your AI agent generate more accurate SAQL queries.

Complex Queries Sometimes Need Refinement: Nested groupings, multi-dataset joins, or advanced SAQL functions can require follow-up prompting to get the exact result you need.

Read-Only During the Beta Program: Create, update, and delete operations aren’t supported yet. If an agent suggests it modified a dataset or dashboard, it didn’t. Treat any such claim as an error.

Missing or Unavailable Assets: If the agent reports that a dataset, dashboard, or lens is unavailable, or a list_ call returns fewer items than you expect, this means you lack permission to that specific asset, not that the server failed. Confirm the app or asset is shared with you and that your permission set grants CRM Analytics access.

Permissions and Governance: MCP sessions use OAuth-authenticated users, meaning any permissions or restrictions that apply to your user profile also apply to your MCP session. Individual authentication is required. Shared service account tokens aren’t supported.

Monitor API Usage: MCP tool calls count against your org’s Wave API limits. Check usage in Setup → API Usage, especially for high-volume workloads.

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