Snowflake MCP Server

The Snowflake MCP server bridges AI assistants with a Snowflake data warehouse, turning raw SQL capabilities into intuitive, context‑aware tools. By exposing database interactions as first‑class MCP resources and tools, it allows assistants to read, write, and explore data without needing custom connectors or manual query crafting. This reduces friction for developers who want AI agents to surface insights, answer business questions, or even modify data on the fly.

At its core, the server offers a read_query tool for executing statements and returning structured results, and a write_query tool (enabled only when write access is granted) that handles , , and . For schema discovery, a suite of tools—, , , and —provides dynamic, up‑to‑date metadata. Developers can also create new tables with create_table and add custom data insights to a continuously updated memo resource via append_insight. The memo () aggregates these insights, making them readily available to the assistant as contextual knowledge.

The server’s resources are designed for quick access and low overhead. The resource, optionally enabled with prefetching, supplies per‑table schema summaries that can be referenced instantly. This means an assistant can answer questions like “What columns does have?” without issuing a separate query. The memo resource acts as a living knowledge base that grows with every insight appended, allowing the assistant to remember and cite observations over time.

Real‑world scenarios benefit from this tight integration: a data analyst can ask an assistant to “Show me the last 10 rows of ,” and receive a formatted table; a product manager can request “Create a new staging table for upcoming reports,” and the assistant will execute on their behalf. In data‑driven workflows, AI can automate routine reporting, trigger alerts when thresholds are crossed, or even perform data cleansing operations—all while maintaining full auditability through the underlying Snowflake permissions.

Unique advantages include seamless schema discovery that keeps assistants in sync with evolving database structures, and the ability to append insights directly into a memo resource, turning raw data analysis into persistent context. The server’s design respects security boundaries: write tools are gated behind an explicit flag, and credentials can be supplied via environment variables or a dedicated connections file. This makes the Snowflake MCP server a robust, developer‑friendly gateway for embedding powerful data capabilities into AI assistants.