When we started building Data Workers, we had to make a foundational decision: how do our AI agents connect to the dozens of tools in a modern data stack? We could build custom integrations for each tool. We could use existing orchestration frameworks. Or we could bet on the Model Context Protocol (MCP).
We bet on MCP. Here is why, and what we are still figuring out.
MCP is an open protocol, originally developed by Anthropic, that standardizes how AI models interact with external tools and data sources. Think of it as a USB-C port for AI — a universal connector that lets an AI agent talk to any tool that implements the protocol.
The ecosystem has exploded. There are now 12,230+ MCP servers available, covering everything from databases to CI/CD tools to cloud platforms. A year ago, this number was in the hundreds.
The math is simple. Data Workers needs to connect to warehouses (Snowflake, Databricks, BigQuery, Redshift), orchestrators (Airflow, Dagster, Prefect), transformation tools (dbt, Spark), catalogs (Unity Catalog, Datahub, Hive Metastore), BI tools (Tableau, Looker, Power BI), and more.
Building and maintaining custom integrations for each of these is a full-time job for a team our size. With MCP, we get a standard interface. If a tool has an MCP server, our agents can connect to it. We are building custom MCP servers for each agent in our swarm.
MCP is the right bet for us. The alternative — building custom integrations — would consume our entire engineering bandwidth. MCP lets a small team connect to a broad tool landscape.
But MCP is not a silver bullet. It solves the connector problem, not the intelligence problem. Our agents still need to know what queries to run, how to interpret results, and when to escalate to a human. MCP gives us the plumbing. We still have to build the logic.
Originally published at https://dataworkers.io/blog/why-we-bet-on-mcp/. Data Workers is an open-source autonomous agent swarm for data engineering — see the repo.