Can my AI actually read the raw table rows via an execute statement?

Yes. When the AI uses `execute_sql` with something like `SELECT * FROM schema.users LIMIT 10`, the MCP integration parses the exact row outputs. The LLM consumes the tabular data back into context so you can converse naturally about the dataset findings.

Is it completely safe to give AI power over a Data Warehouse?

Safety stems from principle of least privilege. Supply a Snowflake Token tied strictly to a read-only role or a heavily scoped down service account. This allows the AI to navigate schemas and extract data without risking destructive schema mutations like DROPs or DELETEs.

Can it search for a column name if I don't know the exact schema?

Yes! Tell your agent: 'Find which table in the SALES_DB database has a column named customer_churn_score'. Due to its autonomous workflow, the bot will pull schemas, subsequently loop over `list_tables`, query Snowflake’s internal information_schema if necessary, and deduce it entirely for you.

Snowflake MCP Server for Google ADK 7 tools — connect in under 2 minutes

Name: Snowflake
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 7 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Snowflake as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="snowflake_agent",
    instruction=(
        "You help users interact with Snowflake "
        "using 7 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Snowflake MCP Server

Connect your Snowflake AI Data Cloud with your AI agent to radically accelerate the way you query large datasets and audit cloud data warehouses. Navigate through deep hierarchical trees of databases, tables, and internal stages natively by chatting with your IDE. Keep your SQL robust by validating commands directly against the live engine.

Google ADK natively supports Snowflake as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 7 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Execute Queries in Chat — Tell your bot to execute_sql based on human prompts or test new complex table joins safely right inside Cursor or Claude
Map Infrastructures — Quickly retrieve spatial contexts by pulling list_databases, traversing downwards through list_schemas to target specific columns
Audit Compute Cost — Keep a firm grip on active clusters running by auditing running instances using list_warehouses
Diagnose Operations — Monitor long-tail data workloads or data engineering pipelines using the get_query_status method asynchronously

The Snowflake MCP Server exposes 7 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Snowflake to Google ADK via MCP

Follow these steps to integrate the Snowflake MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 7 tools from Snowflake via MCP

Why Use Google ADK with the Snowflake MCP Server

Google ADK provides unique advantages when paired with Snowflake through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Snowflake

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine Snowflake tools with BigQuery, Vertex AI, and Cloud Functions

Snowflake + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Snowflake MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Snowflake and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Snowflake tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Snowflake regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Snowflake

Snowflake MCP Tools for Google ADK (7)

These 7 tools become available when you connect Snowflake to Google ADK via MCP:

execute_sql

Prefers read-only statements whenever possible. Executes a SQL query on Snowflake

get_query_status

Retrieves the status of an asynchronous query

list_databases

Lists all databases in the Snowflake account

list_schemas

Lists all schemas within a specific database

list_stages

Lists all internal and external stages

list_tables

Lists all tables within a specific schema

list_warehouses

Lists all virtual warehouses

Example Prompts for Snowflake in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Snowflake immediately.

"List all running virtual warehouses I can access in my Snowflake account."

"Write a query to grab the top 5 most engaged users from our schema and execute it."

"Retrieve the schema mapping for the MASTER_DB. I need to know all nested tables before doing table joints."

Troubleshooting Snowflake MCP Server with Google ADK

Common issues when connecting Snowflake to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Snowflake + Google ADK FAQ

Common questions about integrating Snowflake MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.