Snowflake Cube Server

The MCP Cube Server bridges the gap between AI assistants and the rich semantic layers of a Snowflake‑based data warehouse. By exposing both resources that describe the underlying schema and tools that execute queries against the Cube REST API, this server lets developers treat Snowflake data as first‑class entities in conversational AI workflows. Rather than hard‑coding SQL or building custom connectors, developers can simply ask an assistant to “read data” or “describe the dataset,” and the server will translate those requests into efficient API calls, returning structured JSON or YAML that can be consumed directly by downstream logic.

At its core, the server offers two primary tools. The tool accepts a natural‑language or programmatic query, forwards it to the Cube API, and returns the results in YAML along with a unique . This identifier can then be used to fetch a clean JSON representation via the resource, enabling further formatting or transformation by the client. The second tool, , provides an agentic description of all tables and columns available in the Cube deployment, mirroring the information found in the resource. These capabilities make it trivial for an AI assistant to introspect the data environment, discover available metrics, and construct precise queries on demand.

Developers will find this server particularly useful in scenarios that require dynamic data exploration, such as building analytics dashboards, generating business reports on the fly, or training models that need up‑to‑date statistics. By treating data queries as first‑class tool calls, developers can maintain a clean separation between business logic and data access, reducing boilerplate code and minimizing the risk of SQL injection. The server’s design also supports incremental data retrieval: a single query can yield multiple references, allowing the assistant to batch fetch or stream results without re‑querying.

Integration into existing AI pipelines is straightforward. An MCP‑enabled assistant can invoke or , parse the YAML response, and then feed the resulting JSON into downstream transformers, visualizers, or policy engines. Because the server returns both human‑readable descriptions and machine‑processable data, it serves as a natural pivot point between conversational intent and programmatic execution. Its lightweight REST interface means it can be hosted on any infrastructure that supports MCP, making it a versatile component for both cloud‑native and on‑premises deployments.

In summary, the MCP Cube Server turns Snowflake’s semantic layers into an interactive API that AI assistants can query naturally. Its dual‑tool architecture, coupled with robust resource descriptors, empowers developers to build data‑centric conversational experiences without wrestling with low‑level database details.