Cursor + Data Workers: 15 AI Agents in Your IDE

Cursor MCP agents from Data Workers are 15 specialized AI agents that plug into Cursor through the Model Context Protocol. Each agent owns one data engineering domain — schema, lineage, quality, governance, cost, ML, streaming — and they coordinate through MCP to deliver results no single agent could produce alone.

Cursor MCP agents data workflows are transforming how data teams build and maintain pipelines. With Data Workers' 15 MCP-native agents integrated directly into Cursor, your IDE becomes more than a code editor — it becomes an intelligent data engineering platform. Each agent specializes in a different aspect of data infrastructure: one handles schema introspection, another manages lineage, a third runs quality checks, and all 15 coordinate through the MCP protocol to deliver results that no single agent could produce alone. This article explains how every agent works inside Cursor and how to get the most out of each one.

The traditional approach to AI-assisted data engineering gives you a single chatbot with access to your code files. It can autocomplete SQL and suggest Python patterns, but it has no connection to your actual data infrastructure. Data Workers takes a fundamentally different approach: instead of one general-purpose assistant, you get 15 specialized agents that each understand a specific domain. The Catalog Agent knows your schemas. The Lineage Agent knows your DAGs. The Quality Agent knows your test results and freshness scores. When Cursor invokes these agents through MCP, it gets grounded, accurate responses instead of hallucinated guesses.

How MCP Agents Work Inside Cursor

When you configure Data Workers in Cursor, each of the 15 agents registers as an MCP server. Cursor's AI can invoke any agent by calling its MCP tools — the same way a human would call an API. The key difference from traditional tool-calling is that Data Workers agents can also invoke each other. When you ask a question that requires schema knowledge, lineage context, and quality data simultaneously, the agents coordinate automatically.

In practice, this means you can type a natural language prompt in Cursor's chat — something like 'Create a dbt model that calculates monthly churn rate' — and the agents work together behind the scenes: the Catalog Agent identifies the relevant tables, the Lineage Agent checks upstream dependencies, the Semantic Agent retrieves the canonical churn definition, and the Transformation Agent generates the SQL with correct refs, filters, and materializations. The result appears in Cursor's editor as a complete, correct dbt model.

The 15 Data Workers Agents and What They Do in Cursor

Each agent is purpose-built for a specific data engineering domain. Here is how they map to Cursor workflows:

Using the Agent Picker in Cursor

Cursor's MCP integration exposes Data Workers agents through the tool-calling interface. When you type a prompt in Cursor's chat, the AI automatically selects which agents to invoke based on the task. However, you can also direct specific agents by prefixing your prompt. For example, typing '@quality Check freshness of the orders table' routes the request directly to the Quality Agent, bypassing the routing logic.

For complex tasks that span multiple domains, let the Context Agent handle routing. It analyzes your prompt, determines which agents need to be involved, orchestrates the calls in the optimal order, and assembles the responses into a coherent result. The Context Agent is what makes the 15-agent swarm feel like a single, omniscient assistant — it hides the complexity of multi-agent coordination behind a natural language interface.

Real-World Workflow: Building a New dbt Model

Let us walk through a concrete example. You need to create a dbt model that calculates customer lifetime value. In Cursor, you type: 'Create a dbt model for customer lifetime value using our orders and subscriptions data.'

Here is what happens behind the scenes. The Context Agent receives the prompt and identifies that it needs schema, semantic, lineage, and transformation context. It dispatches parallel requests to four agents. The Catalog Agent returns the schema for orders and subscriptions tables, including column types, descriptions, and row counts. The Semantic Agent returns the canonical CLV formula defined in your business glossary. The Lineage Agent checks whether upstream models are fresh and identifies the correct refs. The Transformation Agent assembles everything into a dbt model with the correct SQL, ref macros, materialization strategy, and schema.yml entry. The result appears in Cursor as a new file, ready for review.

Real-World Workflow: Debugging a Failing Pipeline

You receive a Slack alert that the daily revenue dashboard is showing stale data. Open Cursor and type: 'Why is the revenue dashboard stale? Trace the issue.'

The Observability Agent checks pipeline run history and finds that the stg_orders model failed at 3:47 AM. The Lineage Agent traces downstream and confirms that fct_revenue, rpt_daily_revenue, and the Looker dashboard all depend on stg_orders. The Quality Agent checks the source table and finds that the upstream ingestion completed successfully — so the issue is in the transformation, not the source. The Transformation Agent retrieves the error log and identifies a schema change in the source: a new column was added that conflicts with a SELECT * statement. It suggests the fix, Cursor presents it as an inline edit, and you tab-to-accept. The entire diagnosis took under a minute.

Context Panel: Seeing What Agents Know

One of the most powerful features of Data Workers in Cursor is the context that agents surface during interactions. When you hover over a table reference in your SQL, Cursor can show metadata pulled from the Catalog Agent: row count, last refresh time, column descriptions, and quality scores. When you hover over a ref in a dbt model, the Lineage Agent shows the upstream DAG and downstream dependents.

This contextual information is not static documentation — it is live metadata pulled through MCP at the moment you request it. If a table was refreshed five minutes ago, you see that. If a quality test started failing an hour ago, you see that too. This eliminates the constant context-switching between your IDE, your warehouse console, your catalog tool, and your orchestrator dashboard.

Inline Suggestions Powered by Agent Context

Cursor's inline suggestion engine becomes significantly more accurate with Data Workers agents providing context. When you start typing a SQL query, the Catalog Agent feeds live schema information into the completion engine. Instead of suggesting column names from stale training data, Cursor suggests columns that actually exist in your current schema, with the correct types and naming conventions.

The Semantic Agent further improves suggestions by injecting business context. If you type SELECT revenue in a model that the business glossary associates with net revenue, the completion will suggest finance_metrics.net_revenue rather than orders.gross_amount. This semantic grounding is what transforms Cursor from a smart autocomplete into an intelligent data assistant that understands your business.

Performance and Resource Considerations

Running 15 MCP agents locally does not mean running 15 heavy processes. Data Workers agents are lightweight Node.js services that start on demand and share a single process pool. On a typical developer machine, the full suite uses under 200MB of memory. Agents that are not actively serving requests are suspended and resume instantly when called.

For enterprise teams, Data Workers also supports a remote MCP server mode where agents run on shared infrastructure. This is useful when warehouse credentials need to be centrally managed or when the catalog index is too large for individual laptops. Cursor connects to the remote MCP server the same way it connects to a local one — the configuration just points to a different endpoint.

Getting Started with 15 Agents in Cursor

Setting up Data Workers in Cursor takes under five minutes. Install the package, configure the MCP server in Cursor's settings, provide your warehouse credentials, and start prompting. The Cursor Setup guide walks through every step with screenshots. Once connected, explore the agent capabilities by asking Cursor questions about your data — start with 'What tables do I have?' and work up to complex multi-agent workflows. Visit our Product page for the full agent documentation, or book a demo to see all 15 agents working in a live Cursor session.