Tableau Next
The analytics/tableau-next server exposes Tableau Next's semantic layer and analytics capabilities to AI agents. It provides tools to discover semantic models (SDMs), retrieve dashboard and visualization metadata, explore metrics and KPIs, and execute natural-language analytical questions via the Analytics Agent. This is the bridge between AI agent conversations and the structured analytical layer that Tableau Next maintains over your data.
Tableau Next's semantic model is the key concept here. An SDM defines the business meaning of your data: how tables relate, what metrics mean, how KPIs are calculated, and what terminology maps to what fields. When an agent queries through this server, it operates at the level of business concepts ("revenue by region," "customer churn rate") rather than raw SQL or object fields. This makes the server particularly effective for analytics use cases where the right answer depends on how metrics are defined by your data team, not just what's in the database.
Using Tableau Next MCP provides several key benefits:
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Interoperability Across Agentic Ecosystems: Concierge: Analytics Q&A engine gives your AI agents secure, natural language access to governed data. This integration allows agents to generate answers directly from your Tableau Next models within your preferred AI tools.
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Grounded AI Responses: Empower your AI agent responses with a ready-to-use semantic layer.
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Metadata-Rich Discovery: Enable agents to understand dashboards, KPIs, and analytical assets directly from Tableau Next for better context and discovery.
Common questions about Tableau Next 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. Tableau Next MCP Server uses the protocol to expose Tableau Next analytical capabilities to external AI agents.
Tableau Next MCP uses the data privacy and security tools provided by the AI Platform in Salesforce. Each interaction is protected by the same enterprise-grade standards as your current Tableau Next environment.
Concierge: Analytics Q&A is exposed as an Analytics Q&A tool, making its functionality available externally through any MCP client. In short: Concierge powers Tableau Next's own conversational experience, and Tableau Next MCP Server lets other AI systems call that same capability. When you use Tableau Next MCP, you're using Concierge through a different frontend interface.
- Production:
https://api.salesforce.com/platform/mcp/v1/analytics/tableau-next - Sandbox/Scratch:
https://api.salesforce.com/platform/mcp/v1/sandbox/analytics/tableau-next
- "What analytics models and dashboards are available?"
- "What is our current win rate by region according to the Sales Performance semantic model?"
- "Show me the revenue trend for the last 6 months from the Revenue semantic model."
- "What KPIs are defined in the Customer Health model?"
- "Find dashboards related to pipeline and show me the key metrics they track."
- "Ask the Analytics Agent: what drove the spike in churn last quarter?"
The analytics/tableau-next server provides a suite of tools that allow external AI agents to interact with your analytical ecosystem. 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 are my best product sales in 2025?", the AI agent uses the Analytics Q&A tool analyze_data to query your governed data via Concierge. The integration returns accurate answers and visualizations directly within your chat interface.
For documentation purposes, the available tools are grouped by their functional role within an analytical workflow.
External MCP-compliant AI agents use these tools to query data and explore visual assets.
Executes a natural-language question via the Tableau Analytics Agent against a target semantic model. Use this when the user asks an open-ended analytical question and a semantic model exists that covers the relevant data. The Analytics Agent interprets the question, selects appropriate metrics and dimensions, executes the analysis, and returns results in a structured format.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
utterance | string | Yes | The natural-language question to analyze (e.g., "What drove the increase in churn last quarter?") |
target_entity_type | string | Yes | Must be sdm |
target_entity_name_or_id | string | Yes | API name or ID of the target semantic model |
Outputs: Structured analytical response — the interpreted question, data used, computed results, and a narrative summary.
Lists Tableau Next dashboards available to the current user — metadata only, for discovery and selection. Use this to find relevant dashboards before retrieving a specific one's details.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
| (none) | — | — | No parameters required |
Outputs: Array of dashboard descriptors — ID, API name, display name, and description.
Retrieves details for a single Tableau Next dashboard. Use this after identifying the relevant dashboard via list_dashboards to get its full metadata, including which visualizations it contains.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
dashboardIdOrApiName | string | Yes | The ID or API name of the dashboard |
Outputs: Dashboard details — name, description, workspace, and contained visualization references.
Lists all visualizations (worksheets) available across Tableau Next — metadata only. Use this to discover available charts and analyses, or to find a specific visualization to retrieve in detail.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
| (none) | — | — | No parameters required |
Outputs: Array of visualization descriptors — ID, API name, display name, and description.
Retrieves details for a single Tableau Next visualization. Use this to understand what a specific chart or worksheet contains and how it's configured.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
visualisationIdOrApiName | string | Yes | The ID or API name of the visualization |
Outputs: Visualization details — name, type, data source, and configuration metadata.
External MCP-compliant AI agents use these tools to understand Tableau Next's schemas, relationships, and logical views. The agents use this data architecture to make sense of the tables and connections inside Tableau Next.
Lists all semantic data models (SDMs) available to the current user. Call this first to discover what analytical models exist before drilling into dimensions, measures, or metrics. An SDM represents a curated business view of data — start here to understand what analytical topics are available.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
| (none) | — | — | No parameters required |
Outputs: Array of semantic model descriptors — ID, API name, display name, and description.
Retrieves the full profile of a single semantic model, including business preferences and configuration. Use this to understand the purpose and scope of a specific model before querying its dimensions, measures, or metrics.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
Outputs: Semantic model profile — name, description, business preferences, and configuration metadata.
Lists the data objects (entities/tables) defined within a semantic model. Call this to understand the data model structure — what entities the SDM contains — before querying for dimensions or measures on a specific object.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
Outputs: Array of data objects in the model, each with ID, API name, and display name.
Lists relationships between data objects in a semantic model. Use this to understand how entities join — for example, how Orders relates to Customers, or how Products relates to Transactions. Helpful before constructing multi-entity analytical queries.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
Outputs: Array of relationship descriptors — from object, to object, join type, and cardinality.
Retrieves the structure of a single logical view within a semantic model. Logical views define how data is organized and exposed within an SDM — use this for deep inspection of a specific view's structure.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
logicalViewIdOrApiName | string | Yes | The ID or API name of the logical view |
Outputs: Logical view structure including fields, data sources, and configuration.
External MCP-compliant AI agents use these tools to identify specific Tableau Next KPIs and calculation logic.
Lists measures for a specific data object in a semantic model. Measures are numeric fields used for aggregation — for example, Revenue, Deal Count, or Customer Lifetime Value.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
dataObjectIdOrApiName | string | Yes | The ID or API name of the data object |
Outputs: Array of measure descriptors — name, aggregation type, description, and unit.
Lists dimensions for a specific data object in a semantic model. Dimensions are categorical, date, or text fields used for grouping and slicing data — for example, Region, Product Category, or Time Period.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
dataObjectIdOrApiName | string | Yes | The ID or API name of the data object |
Outputs: Array of dimension descriptors — name, type, description, and usage guidance.
Lists business metrics and KPIs defined in a semantic model. Metrics are named, governed calculations — the official definition of how a business measure is computed, often corresponding to targets, OKRs, or executive dashboards.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
Outputs: Array of metric descriptors — name, description, calculation definition, and target values if configured.
Retrieves the full definition of a single metric or KPI — including how it is calculated, what data it draws from, and any thresholds or targets configured. Use this when you need to explain what a metric means or verify the calculation behind a number.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
metricIdOrApiName | string | Yes | The ID or API name of the metric |
Outputs: Full metric definition — name, description, formula, data source, and configured targets.
Lists calculated dimensions in a semantic model — user-defined expressions that yield categorical, date, or text results. These are derived fields that don't exist in the raw data but are defined in the semantic layer for analytical consistency.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
Outputs: Array of calculated dimension descriptors with their expressions and descriptions.
Lists calculated measures in a semantic model — user-defined numeric expressions or aggregations. These are derived metrics defined in the semantic layer, such as "Win Rate" or "Average Deal Size."
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
modelIdOrApiName | string | Yes | The ID or API name of the semantic model |
Outputs: Array of calculated measure descriptors with their formulas and descriptions.
External MCP-compliant AI agents use these tools to navigate Tableau Next's environment and search for relevant analytical assets.
Lists Tableau Next workspaces available to the current user, or retrieves a specific workspace by ID. Workspaces are containers that organize related dashboards, visualizations, and data assets.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
workspaceIdOrApiName | string | No | If provided, returns the specified workspace; otherwise lists all accessible workspaces |
Outputs: Array of workspace descriptors (or a single workspace) — ID, name, and description.
Lists assets contained within a specific workspace — dashboards, visualizations, and other items. Use this after identifying a relevant workspace to see what analytics content it contains.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
workspaceIdOrApiName | string | Yes | The ID or API name of the workspace |
Outputs: Array of asset descriptors — type, ID, name, and description for each asset in the workspace.
Searches for analytics assets (dashboards, visualizations, semantic models) using a keyword and optional type filter. Use this to quickly find relevant content by name or description without browsing the full catalog.
Annotations: readOnlyHint: true · openWorldHint: false
Inputs:
| Parameter | Type | Required | Description |
|---|---|---|---|
query | string | Yes | Search keyword or phrase |
type | string | No | Optional asset type filter (e.g., dashboard, visualization, semanticModel) |
Outputs: Array of matching assets across the Tableau Next catalog — type, ID, name, and relevance summary.
Use these solutions to resolve common issues and optimize the performance of Tableau Next MCP Server across different AI clients.
Server Connection Fails: Verify that Node.js is installed on your environment. It's a mandatory requirement to run the initial command that connects AI clients like ChatGPT or Claude to Tableau Next MCP Server.
Claude Disconnections: Updates to your configuration, or inactivity with Claude for a few hours, can cause the session managed by Tableau Next MCP Server to disconnect. To resolve this, exit and re-open Claude. If the issue persists, click "Search and tools," select "Manage connectors," and reconnect the Tableau Next MCP custom connector.
Performance Expectations: Note that AI clients like Claude desktop can vary in their response time depending on the specific AI client, the model being used, and their connection to the MCP server via HTTPS. For example, Claude Desktop might take about a minute to produce an answer, while native Agentforce is considerably faster, often 10-20 times quicker.
Context Retrieval: If the agent needs to find named business metrics or KPIs rather than row fields, prompt it to use the list_semantic_model_metrics tool.
Stop Claude Tool Prompts: If Claude keeps asking for your approval before running tools, navigate to the "Manage Connectors" setup page, edit the tab-next-mcp connector, and change the tool permissions to "Always allow". Ensure you don't turn off notifications for tools that require manual approval.
Missing Q&A Tools: If you can't access the Analytics Q&A capabilities, verify that Concierge is enabled in your Salesforce Org. Users are only able to see and work with the tools they have permissions to use.
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. The MCP enforces your standard Tableau user permissions, semantic model definitions, and Trust Layer protections.
"Unavailable for you at the moment" Error: If you receive a message stating an item is unavailable, this specifically indicates a lack of permissions for that asset. If this happens, review the MCP documentation for further details.