Snowflake MCP Server
Connect to the Snowflake MCP server to run SQL queries, transform data, inspect schemas, and bulk load tables using AI agents on Gumloop, Claude, or Cursor.
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Snowflake
Installation
Get StartedCreate a Gumloop Account
To use this MCP, you need a Gumloop account. If you don't have one yet, you can create one for free.
Copy Your Server URL
Copy your MCP server URL and add it to your client. You'll be prompted to authorize on first use.
Tools (3)
Describe Table
Describe structure of a table
Execute Query
Execute a SQL query (Ensure its valid snowflake sql syntax)
Stage Data
Bulk load data into a table via staging.
What is Snowflake MCP?
The Snowflake MCP server gives AI agents access to your Snowflake data warehouse. That means agents can run any Snowflake SQL your role permits, read the structure of a table down to its columns, types, and constraints, insert, update, and delete records, create and alter databases, schemas, tables, and warehouses, and bulk load fresh data into a table. It works across the full Snowflake hierarchy of databases, schemas, tables, columns, and warehouses, so an agent can explore your account before it queries it.
If your team spends time writing one-off queries, hand-building CSV load scripts, copying results into spreadsheets, or waiting on an engineer to spin up a warehouse and run a transformation, an AI agent can take over a lot of that work. Describe what you need, and your AI agent will handle the SQL, the loads, and the cleanup for you.
MCP stands for Model Context Protocol. It’s an open standard that gives AI agents a way to connect to external tools and services. Instead of installing the Snowflake driver, managing connection strings, juggling OAuth tokens, and coding against the SQL API yourself, you connect your Snowflake account once. After that, you can query and manage your warehouse just by chatting with your AI agent.
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What you can do with Snowflake MCP on Gumloop
Run any Snowflake SQL with your AI agent
Execute arbitrary Snowflake SQL through a single tool: SELECT queries, JOINs, aggregations, window functions, and CTEs. The agent acts as a warehouse copilot that writes and runs the query for you. There is no row limit enforced by the server, so the agent includes a LIMIT when you want a sample instead of the full table.
Transform and modify data
Insert, update, delete, and merge records with standard SQL. An agent can clean a column, backfill a value, or reshape a table in place, then report how many rows changed.
Create and change database objects
Run DDL to create, alter, and drop databases, schemas, tables, views, and warehouses. An agent can stand up a staging table, add a column, or provision compute for a one-time job.
Inspect table structure
Describe any table to see its columns, data types, nullability, primary and unique keys, defaults, and comments. Agents use this to understand a schema before writing a query against it.
Bulk load data into a table
Load data in bulk through Snowflake’s internal stage with a PUT and COPY INTO pipeline. Pass an inline array of records or point to a Gumloop-managed CSV file, and the agent handles staging and loading, matching columns by name (case-insensitive) by default.
Browse your warehouse before you query
Navigate a hierarchy of databases, schemas, tables, and columns, plus your warehouse inventory, through MCP resources. Agents discover what exists in your account instead of guessing at object names.
Keep an audit trail of AI-generated queries
Every query is tagged automatically with Gumloop context (user, project, agent, and interaction) in Snowflake’s QUERY_HISTORY, with an optional /* generated by Gumloop */ comment that is added by default. Your data team can attribute and audit exactly what an agent ran.
How to connect the Gumloop Snowflake MCP Server
- 1
Create a free Gumloop account
Sign up at gumloop.com. No credit card required.
- 2
Add the Snowflake MCP server
Copy your MCP server URL from Gumloop and add it to your preferred client (Claude, Cursor, or Gumloop workflows). You'll authorize on first use.
- 3
Start using Snowflake in your AI workflows
That's it. Your AI agent can now run SQL, transform data, inspect schemas, and bulk load tables across your Snowflake account. Use it inside a Gumloop automation, in Claude Desktop, or in Cursor.
Snowflake MCP use cases
Self-serve analytics for data analysts
An analyst can ask a Gumloop agent a question in plain terms, and the agent inspects the relevant table, writes the SQL, runs it against Snowflake, and returns the numbers. Pair it with Google Sheets or Slack so the agent drops a summarized result where the team already works, no manual export required.
Bulk data loading for data engineers
When a fresh dataset lands, a Gumloop agent can take an inline array of records or a Gumloop-managed CSV file and bulk load it into the target table through the staging pipeline, then report rows loaded, rows parsed, and any errors seen. Engineers stop hand-writing PUT and COPY INTO scripts for routine loads.
Schema management and DDL for data platform teams
A platform team can have an agent stand up a staging table, add a column to an existing table, or provision a warehouse for a one-time backfill, all from a plain description. Because every statement is tagged in QUERY_HISTORY, the team keeps a clean record of what changed and who triggered it.
Ad-hoc data exploration for data scientists
A data scientist can point an agent at a database, let it browse schemas and describe candidate tables, and then iterate on exploratory queries with window functions and CTEs. The agent narrows a wide table to a sampled LIMIT while the scientist refines the question.
Cross-platform reporting agents
Connect Snowflake with Slack, Gmail, Google Sheets, and other MCP integrations in a single agent. An agent can query a daily metric in Snowflake, compare it against last week, write the result to a sheet, and post the headline to a Slack channel, so your warehouse talks to the rest of your stack automatically.
Why use Gumloop for Snowflake MCP
Connect once, stored securely
Most Snowflake MCP servers you’ll find on GitHub make you install the Snowflake driver, manage connection strings, and write code to handle auth and token refresh. With Gumloop you connect your Snowflake account once (OAuth or username and password plus your account identifier), stored securely. No env vars, connector setup, or coding against the Snowflake driver necessary. With OAuth, your tokens refresh automatically on every call.
Works with multiple MCP clients
Use the Snowflake MCP server endpoint in Claude Desktop, Cursor, or directly inside Gumloop agents. Same server URL, works with any MCP client.
Chain Snowflake with 100+ other integrations
Combine Snowflake with Slack, Gmail, Google Sheets, Notion, and other MCP integrations in a single AI agent. An agent can pull data from one tool, process it with an LLM, and write the result back to Snowflake or anywhere else.
Enterprise-grade and scalable
Built for teams, with role-based permissions and dedicated support for Pro users. The Snowflake MCP server respects your existing Snowflake access controls, so an agent can only reach what your connected role can. For details on Gumloop’s security practices, see trust.gumloop.com.
Pricing includes a free plan
You can test the Snowflake MCP integration on Gumloop’s free tier before committing. Paid plans start at $37/month.
Frequently asked questions
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