Connect your account, then chat with AI to run tools.
Explore Snowflake warehouses, databases, schemas, tables, and run SQL analytics workloads from chat.
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Live probe refreshed Sep 18, 2026 · Endpoint host mcp.mcpbundles.com
Built for
Data Teams, Analytics Engineering, BI Operations, Platform Administration
Explore warehouse structure
Uses discovery tools before writing SQL.
List Snowflake databases, schemas, tables, and warehouses, then summarize the likely analytics domains available in this account.
Connect Snowflake to any MCP client in minutes
You’ll sign in to MCPBundles when your client connects.
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You’ll sign in to MCPBundles when your client connects.
https://mcp.mcpbundles.com/bundle/snowflakeThis server needs a sign-in. MCPBundles handles that setup once — then chat here with saved access and a log of what ran.
Opens MCPBundles Studio with this server selected. After sign-in, chat and run tools from the same thread.
Browse all toolsDescribe a table before querying
Reduces SQL mistakes by inspecting schema first.
Describe this Snowflake table, explain the important columns and types, then draft a safe SELECT query for the business question.
Run an analytics query
Connects warehouse execution to business analysis.
Execute a Snowflake SQL query to summarize monthly revenue by customer segment, then explain the result and caveats.
Review warehouse usage context
Highlights compute boundaries before query execution.
List warehouses and explain which one should be used for an exploratory query versus a heavier analytics workload.
What Snowflake objects can agents inspect?
Agents can inspect warehouses, databases, schemas, tables, table columns, and execute SQL statements against the connected Snowflake account.
Why should agents list metadata before querying?
Snowflake accounts can contain many databases and schemas. Listing metadata first helps select the right warehouse, database, schema, and table before running SQL.
Can agents run arbitrary SQL?
The server executes SQL under the connected role and warehouse context. Prompts should be explicit about read-only analysis versus DDL or DML changes.
Related editorial
Snowflake Workflows with AI
How AI agents can run SQL, discover schemas, manage warehouses, and read query history to answer data questions and tune performance.
Domain knowledge for Snowflake — workflow patterns, data models, and gotchas for your AI agent.
Cloud data warehouse with separation of storage and compute, addressed by your account URL. All operations go through the Snowflake SQL REST API — every tool ultimately executes SQL statements.
Start here
Snowflake
Snowflake Account Info
snowflake_account_infoGet current Snowflake session information: account name, user, role, warehouse, database, schema, and Snowflake version.
Open in StudioAgents can inspect warehouses, databases, schemas, tables, table columns, and execute SQL statements against the connected Snowflake account.
Snowflake accounts can contain many databases and schemas. Listing metadata first helps select the right warehouse, database, schema, and table before running SQL.
The server executes SQL under the connected role and warehouse context. Prompts should be explicit about read-only analysis versus DDL or DML changes.
Identifiers are uppercased by default, warehouses must be available for queries, and semi-structured data requires Snowflake's VARIANT path syntax.
Add the MCPBundles server URL to your MCP client configuration (Claude Desktop, Cursor, VS Code, etc.). The URL format is: https://mcp.mcpbundles.com/bundle/snowflake. Authentication is handled automatically.
Snowflake provides 28 tools that can be called by AI agents, along with a SKILL.md that gives your AI agent domain knowledge about when and how to use them.
Snowflake uses API Key. Snowflake requires credentials. Connect via MCPBundles and authentication is handled automatically.
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