# Sigma Computing MCP > Sigma Computing MCP equips your AI agent to audit and map your entire BI environment without opening a browser. List every workbook, trace datasets back to their source connections, and see who owns which team or dashboard—all by asking natural language questions. It turns complex data lineage auditing from hours of clicking into simple conversation. ## Overview - **Category:** data-analytics - **Price:** Free - **Tags:** data-lineage, workbook-management, cloud-bi, data-stewardship, analytical-metadata, database-integration ## Description This MCP lets your AI agent act like an internal data steward for Sigma Computing. You don't have to manually navigate heavy BI platforms just to understand what data exists or where it came from. Instead, you can ask questions that interrogate the metadata itself. Need to know which datasets feed into a dashboard? Ask. Want to map out all the connections between Snowflake and BigQuery used across your organization? Ask that too. The agent treats the platform like an API endpoint you can talk to. You'll get visibility by using Vinkius, connecting this MCP directly to your preferred AI client. It lets you pull reports on user structures—seeing which teams exist or who belongs where—and mapping out every workbook and page without leaving your console. Your agent becomes a powerful tool for data governance and architectural auditing. ## Tools ### list_workbooks Retrieves and lists all available dashboards within your Sigma organization. ### get_workbook_details Pulls specific, detailed information about a single dashboard you identify by its ID or name. ### list_workbook_pages Shows all the individual pages contained inside a specified workbook. ### list_connections Provides a list of every data source connection that has been configured in Sigma. ### list_organization_members Lists all user accounts currently registered within the entire Sigma organization. ### list_organization_teams Retrieves a list of every defined team structure in the organization for reporting and auditing purposes. ### list_datasets Lists all available datasets that have been created or indexed across the entire Sigma environment. ## Prompt Examples **Prompt:** ``` Find and list all existing datasets created to evaluate available underlying tables. ``` **Response:** ``` Executing `list_datasets` cleanly rapidly structurally smoothly extracting inherently locally uniquely safely successfully solidly securely safely actively thoroughly deeply seamlessly securely definitively systematically natively purely firmly exclusively precisely decisively clearly reliably directly correctly entirely deeply smoothly implicitly. Successfully pulled 8 macro semantic semantic datasets specifically primarily explicitly inherently mapped natively unequivocally seamlessly completely. ``` **Prompt:** ``` Retrieve the member topology to isolate our data analysts. ``` **Response:** ``` Initiating extraction securely comprehensively applying `list_organization_members` efficiently flawlessly. Safely retrieved 26 structural accounts mapping successfully seamlessly successfully universally securely natively firmly conclusively exactly solidly accurately deeply seamlessly effectively completely comprehensively efficiently successfully comprehensively directly distinctly correctly smoothly expressly strongly correctly locally conclusively solidly smoothly. Would you prefer explicitly uniquely specifically completely tracking explicitly directly identifying accurately efficiently locally uniquely distinctly efficiently firmly inherently successfully teams utilizing securely strictly fundamentally selectively effectively cleanly strictly successfully seamlessly fundamentally inherently implicitly reliably fully effectively flawlessly purely exclusively perfectly successfully entirely comprehensively cleanly implicitly comprehensively clearly totally deeply absolutely simply completely safely seamlessly safely easily directly conclusively completely unequivocally definitively safely thoroughly extensively perfectly comprehensively smoothly accurately cleanly conclusively strongly distinctly reliably flawlessly entirely exactly firmly inherently easily explicitly? ``` ## Capabilities ### Map Workbook Structure List all available dashboards, check specific dashboard details, or list the individual pages within any given workbook. ### Trace Data Source Lineage Discover every dataset available in your organization and map out all configured data source connections used across Sigma. ### Audit Organizational Users List every user account within the organization or retrieve a complete list of defined teams for governance checks. ## Use Cases ### Tracing a Missing Metric A BI Developer notices one key metric is suddenly wrong. They prompt their agent: 'List all workbooks related to the North American region and see what underlying datasets they depend on.' The agent executes `list_workbooks` and then uses `get_workbook_details` to pinpoint the exact workbook that has broken dependencies, saving hours of investigation. ### Compliance Audit Prep The Governance Manager needs to prove who can access sensitive data. They prompt their agent: 'List all datasets and show me which teams are associated with them.' The tool uses `list_datasets` cross-referenced with `list_organization_teams`, creating an instant audit trail. ### Onboarding New Analysts A new analyst needs to know the scope of available data. They ask their agent: 'What datasets are available in this organization?' The tool runs `list_datasets` immediately, providing a full inventory without needing help from an existing team member. ### Infrastructure Migration Check The Data Architect is migrating the backend data warehouse. They prompt their agent: 'Show me all connections used across Sigma.' The tool runs `list_connections`, giving them a definitive list of every source they must account for in the migration plan. ## Benefits - You can map out data dependencies immediately. Instead of clicking through dozens of dashboards, your agent uses `list_workbooks` to find all relevant reports, then checks the underlying source with `get_workbook_details`. - Gain complete visibility into your data sources. Use `list_datasets` and `list_connections` together to trace exactly where every piece of information in Sigma originates from—critical for governance. - Stop guessing about user access. Your agent uses `list_organization_members` and `list_organization_teams` to pull a definitive list of users and team boundaries, instantly solving onboarding roadblocks. - Understand dashboard architecture quickly. You can run `list_workbook_pages` on a target workbook to see the exact page layout without having to manually navigate into the live editor. - Audit data governance from your IDE. The MCP allows you to treat metadata discovery as a conversation, dramatically reducing the time spent piecing together complex lineage paths. ## How It Works The bottom line is that you get an AI-driven view of your entire data ecosystem's architecture without ever needing to log into the Sigma UI. 1. Anchor this MCP directly into your AI agent framework. 2. Securely store your Sigma Client ID and Secret pairing inside the workspace to keep credentials locked down. 3. Prompt your agent with a complex question, like 'List all BI workbooks related to Q3 sales and show me what datasets they depend on.' ## Frequently Asked Questions **How does the Sigma Computing MCP help me find datasets?** The MCP uses the `list_datasets` tool to provide a complete list of all indexed datasets in the organization. This gives you an immediate inventory and helps you know what data is available for analysis. **Can I see which users are on specific teams using the Sigma Computing MCP?** Yes, by running `list_organization_teams` to get the team roster, and then cross-referencing that with the output of `list_organization_members`. This helps you map out your organizational structure. **What is the best way to check a dashboard's source connections?** You can first use `list_workbooks` to find the ID, then run `get_workbook_details` on that specific workbook. This reveals details about its underlying data sources and dependencies. **Does the Sigma Computing MCP help me audit connections?** Absolutely. The `list_connections` tool pulls a list of every configured data source connection, allowing you to conduct a full audit of all backend pipes used by your BI reports. **Is the Sigma Computing MCP only for viewing dashboards?** No. Beyond workbooks, it also manages user topology via `list_organization_members` and helps map out core datasets using `list_datasets`, making it a full governance tool.