How to integrate Snowflake MCP with LangChain

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Introduction

This guide walks you through connecting Snowflake to LangChain using the Composio tool router. By the end, you'll have a working Snowflake agent that can run a sql query to list today's new users, cancel a long-running data import statement, show all unresolved incidents in snowflake through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Snowflake account through Composio's Snowflake MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Snowflake with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Snowflake project to Composio
  • Create a Tool Router MCP session for Snowflake
  • Initialize an MCP client and retrieve Snowflake tools
  • Build a LangChain agent that can interact with Snowflake
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Snowflake MCP server, and what's possible with it?

The Snowflake MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Snowflake account. It provides structured and secure access to your cloud data warehouse, so your agent can run complex SQL queries, monitor system health, check scheduled maintenances, and manage incidents seamlessly—no manual intervention needed.

  • Automated SQL execution and data retrieval: Direct your agent to execute SQL statements and instantly fetch query results from your data warehouse.
  • Query management and cancellation: Have your agent monitor and cancel long-running or stuck SQL statements to keep your workflows running smoothly.
  • Maintenance and system status monitoring: Let your agent check for active, upcoming, or completed scheduled maintenances and get real-time updates on system components.
  • Incident detection and reporting: Enable your agent to retrieve unresolved incidents and receive summaries of any issues currently affecting your Snowflake environment.
  • Integration metadata access: Fetch details about catalog integrations and system status rollups so your agent can keep tabs on the overall health of your Snowflake setup.

Supported Tools & Triggers

Tools
Cancel Statement ExecutionCancels the execution of a running SQL statement.
Check Statement StatusRetrieves the status and results of a previously submitted SQL statement using its statement handle.
Execute SQLExecute SQL statements in Snowflake and retrieve results.
Fetch Catalog IntegrationRetrieves detailed configuration and metadata for a specific catalog integration.
Get Active Scheduled MaintenancesRetrieves a list of any active scheduled maintenances currently in the In Progress or Verifying state.
Get All Scheduled MaintenancesRetrieves a list of the 50 most recent scheduled maintenances, including those in the Completed state.
Get Component StatusRetrieves the status of individual components, each listed with its current status.
Get Status RollupRetrieves the status rollup for the entire page, including indicators and human-readable descriptions of the blended component status.
Get Status SummaryRetrieves the current status summary from Snowflake's public status page (status.
Get Unresolved IncidentsRetrieves a list of any unresolved incidents from the Snowflake status page.
Get Upcoming Scheduled MaintenancesRetrieves upcoming scheduled maintenances from Snowflake's public status page.
Show DatabasesLists all databases for which you have access privileges.
Show SchemasLists all schemas for which you have access privileges.
Show TablesLists all tables for which you have access privileges.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Snowflake functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Snowflake tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Snowflake
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['snowflake']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Snowflake tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Snowflake tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "snowflake-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Snowflake MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Snowflake tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Snowflake related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Snowflake and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['snowflake']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "snowflake-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Snowflake related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Snowflake through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

What are the differences in Tool Router MCP and Snowflake MCP?

With a standalone Snowflake MCP server, the agents and LLMs can only access a fixed set of Snowflake tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Snowflake and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Snowflake tools.

Can I manage the permissions and scopes for Snowflake while using Tool Router?

Yes, absolutely. You can configure which Snowflake scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Snowflake data and credentials are handled as safely as possible.

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