How to Use the Snowflake MCP in LlamaIndex

Q: Can I use LlamaIndex to audit my Snowflake setup?

Yes. By indexing the output of listdatabases and listschemas, LlamaIndex provides a searchable record of your Snowflake environment's structure, making audits much simpler.

Q: If I run an operation using LlamaIndex with Snowflake, where is that data stored?

The MCP tool output—the results from tools like listtables or getsessioncontext—is indexed into the vector store associated with your LlamaIndex application, not back into Snowflake itself.

Q: Does LlamaIndex support checking Snowflake statement status using the MCP Server?

Yes. The getstatementstatus tool is available on the MCP Server, letting your LlamaIndex application check if an asynchronous query run against Snowflake has completed or stalled.

Index Snowflake data with LlamaIndex for knowledge-augmented AI retrieval.

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Indexing Structured Data in LlamaIndex

LlamaIndex treats the output from our MCP Server as searchable knowledge. When you run a query using `execute_sql` against Snowflake, the resulting data can be indexed into your vector store. This means you're not just getting an answer; you're building a persistent record of that specific API interaction. This capability is key for RAG applications: instead of relying on hallucination, LlamaIndex grounds its answers in actual, queryable Snowflake results. You can search past configurations or data points retrieved via the MCP Server.

Querying Metadata with LlamaIndex

LlamaIndex allows you to index metadata about your system's structure. By calling tools like `list_databases` or `list_schemas`, the results become part of a searchable knowledge base. This lets developers ask questions like, 'What databases are available?' and get an answer sourced directly from Snowflake data. It also indexes security information using `list_roles`. This means your LlamaIndex application can help onboard new team members by providing documented answers about the roles present in your Snowflake environment.

Managing Data Lifecycle with MCP Server

The ability to manage data flow is critical for effective indexing. You can use `describe_table` on target tables, and LlamaIndex indexes those schema details. Knowing the column types helps build better retrieval queries. Furthermore, if a query needs manual review or cancellation, calling `cancel_sql` ensures that the system isn't left with dangling processes in Snowflake, keeping your indexed knowledge base clean and accurate.

Setup guide

Set up Snowflake MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Snowflake MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Snowflake tools.",
)
response = await agent.run("List recent Snowflake data")

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Common questions about Snowflake MCP in LlamaIndex

LlamaIndex ingests the structured results from our MCP Server's `execute_sql` tool. These results become vectors in your index, allowing you to perform semantic searches on actual query outcomes against Snowflake.

Yes. By indexing the output of `list_databases` and `list_schemas`, LlamaIndex provides a searchable record of your Snowflake environment's structure, making audits much simpler.

The MCP tool output—the results from tools like `list_tables` or `get_session_context`—is indexed into the vector store associated with your LlamaIndex application, not back into Snowflake itself.

The server handles textual metadata (database names, role names) and structured query results. LlamaIndex ensures these different types are all successfully incorporated into a unified knowledge index.

Yes. The `get_statement_status` tool is available on the MCP Server, letting your LlamaIndex application check if an asynchronous query run against Snowflake has completed or stalled.

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