How to Use the Snowflake MCP in Google ADK

Q: How does Google ADK handle complex data access in Snowflake?

The agent uses executesql to manage data flow. By returning handles for long queries, it supports massive datasets without overwhelming the client or running into timeout limits.

Q: What if I need to list all Snowflake schemas via Google ADK?

You simply call listschemas within a specific database scope. This provides a clear, actionable inventory of organizational data groupings.

Act on Snowflake data using your Google ADK for enterprise agent workflows.

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Connect Snowflake MCP to Google ADK

Create your Vinkius account to connect Snowflake to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Snowflake with Google ADK

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Run and Control Queries

The `execute_sql` tool lets your agent run any necessary query against Snowflake. The result is either the first set of data or a status handle, making it easy to manage long-running jobs. You can pair this with `get_statement_status` to monitor performance and ensure queries finish when expected.

Audit System Metadata

The MCP Server gives your agent full visibility into the data environment. Calling `list_databases` retrieves all accessible databases, while `list_roles` lets you check security boundaries. This is essential for agents building compliant enterprise applications on Google Cloud.

Schema Validation

Need to know what columns are available? Use `describe_table`. It pulls the schema details, confirming data types and column names before an action. This prevents runtime errors caused by unexpected structure changes. It’s a non-negotiable step when integrating with Snowflake.

Setup guide

Set up Snowflake MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Snowflake tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Snowflake_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Snowflake tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Snowflake MCP in Google ADK

The agent uses `execute_sql` to manage data flow. By returning handles for long queries, it supports massive datasets without overwhelming the client or running into timeout limits.

You simply call `list_schemas` within a specific database scope. This provides a clear, actionable inventory of organizational data groupings.

Yes, you can list and check compute warehouses using tools like `list_warehouses`. The agent knows which resources are active or available to run jobs against the MCP Server.

The server handles details on databases, roles, users, schemas, and tables. All these object names are exposed as string identifiers for your agent to process.

It only touches metadata strings, such as user IDs and database definitions. The server doesn't read or transmit actual row-level PII; it maps the container structure.

Use it with your favorite AI tools

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