Connect your AI Agents to Sigma Computing in minutes

What is a Sigma Computing MCP?

It's an MCP server that lets your agents access data and functionality from Sigma Computing via tools. Your agents can invoke these tools to pull a workbook's latest results, list the datasets available in a workspace, check who has access to a given workspace, export a dashboard's underlying data, and more.

Sigma Computing also ships its own MCP server for natural-language search and analysis over workbooks and data models, but Merge Agent Handler lets you combine Sigma Computing tools with every other connector your agents use in one governed endpoint, with credential management, DLP, and audit logging built in.

How can I use the Sigma Computing MCP server?

The use cases naturally depend on the agent you've built, but here are a few common ones:

• Dashboard summary digests: every Monday morning, an agent pulls the latest numbers from a revenue workbook and posts a plain-language summary to Slack, so the team catches up without opening Sigma Computing

• Anomaly detection on a metric: an agent checks a key metric against its historical range on a schedule and flags the workbook when a value moves outside expected bounds, catching a broken pipeline before a stakeholder notices

• Cross-tool data correlation: when a support ticket references a customer, an agent pulls that account's usage numbers from a Sigma Computing dataset and attaches them to the ticket for context

• Access provisioning for new workspace members: when a new analyst joins a team in the HRIS, an agent adds them to the right Sigma Computing workspace and sets their role based on department

What are popular tools for Sigma Computing's MCP server?

Here are some of the most commonly used tools:

• list_workbooks: returns the workbooks a user or service account can see across workspaces. it's the starting point for most agents since almost every other call needs a workbook ID first

• get_workbook_data: pulls the current query results from a specific workbook element. agents use it to answer questions about the latest numbers without re-running the underlying SQL themselves

• list_datasets: lists the datasets and data models available in a workspace, including their descriptions. this is how an agent discovers what data exists before it tries to query anything

• get_dataset_schema: returns column names, types, and definitions for a given dataset. agents rely on this to write accurate queries and explain what a field actually measures

• list_workspace_members: lists who has access to a workspace and at what permission level. it's commonly used for access audits and provisioning checks

• get_embed_url: generates a signed embed URL for a workbook or dashboard. agents use it to hand off a live view of the data instead of describing it in text

What makes Merge Agent Handler's Sigma Computing MCP server better than alternative Sigma Computing MCP servers?

A few things set Merge Agent Handler's Sigma Computing MCP server apart from building your own or using a community alternative:

• Enterprise-grade security and DLP: Merge Agent Handler includes built-in data loss prevention controls that let you block or redact sensitive fields before they reach an agent. For Sigma Computing, this means you can prevent row-level revenue or customer data surfaced in a workbook from being exposed even when the agent has broad read access

• Managed authentication and credentials: Merge stores and refreshes Sigma Computing credentials on your behalf. You never expose raw API keys or OAuth tokens to an agent or manage token rotation manually

• Real-time observability and audit trail: Every tool call made against Sigma Computing is logged with timestamp, tool name, input parameters, and response metadata. You can audit exactly what an agent read or wrote without instrumenting anything yourself

• Tool Packs and controlled access: Tool Packs let you bundle specific Sigma Computing tools with tools from other connectors into a single MCP endpoint, scoped to a specific use case. An agent gets exactly the tools it needs, nothing more

How can I start using Merge Agent Handler's Sigma Computing MCP server?

You can take the following steps:

1. Create or log into your Merge Agent Handler account.

2. Install the Merge CLI by running pipx install merge-api, then run merge configure to link the CLI to your Merge account and merge login to authenticate your session.

3. Register the Agent Handler MCP server with Claude Code by running claude mcp add --transport http agent-handler https://ah-api.merge.dev/mcp, then open Claude Code and run /mcp to confirm agent-handler appears with a connected status.

4. Select agent-handler from the MCP list. This opens a browser window where you select which integrations to authenticate. Choose Sigma Computing and complete the auth flow. Merge stores and manages the credentials going forward.

5. Open a Claude Code session and start querying Sigma Computing data directly. The first time you use a Sigma Computing tool, a Magic Link may appear to complete connector authentication.

If you want to connect Merge Agent Handler's Sigma Computing MCP with internal or customer-facing agentic products, you can follow the steps in our docs.

Can employees use Merge to connect their AI tools to Sigma Computing?

Yes, Merge for Workforce lets your employees connect Claude, ChatGPT, Microsoft Copilot, Cursor, Codex, and other MCP-compatible AI tools to Sigma Computing without bypassing IT governance.

Instead of setting up direct connections with personal credentials that IT can't monitor or revoke, each employee authenticates through Merge and gets individual credentials tied to their identity. A data analyst can ask their AI to pull a workbook's latest numbers, summarize a dashboard, or check a metric trend, all within IT-defined permissions.

IT also provisions access by role or group via SCIM. A data analyst, for example, gets Sigma Computing access to query workbooks, Snowflake access to run ad hoc SQL, and Slack access to post dashboard summaries, while a sales manager gets Salesforce access to pull pipeline data, Sigma Computing access to check quota dashboards, and Gmail access to draft forecast updates.

Every tool call an employee's AI makes to Sigma Computing is also inspected against your DLP rules and logged to a searchable audit trail, giving security teams full visibility into what data was accessed and by whom.