# How to integrate Snowflake MCP with Autogen ```json { "title": "How to integrate Snowflake MCP with Autogen", "toolkit": "Snowflake", "toolkit_slug": "snowflake", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/snowflake/framework/autogen", "markdown_url": "https://composio.dev/toolkits/snowflake/framework/autogen.md", "updated_at": "2026-05-12T10:26:42.921Z" } ``` ## Introduction This guide walks you through connecting Snowflake to AutoGen 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 AutoGen 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 - [ChatGPT](https://composio.dev/toolkits/snowflake/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/snowflake/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/snowflake/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/snowflake/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/snowflake/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/snowflake/framework/codex) - [Cursor](https://composio.dev/toolkits/snowflake/framework/cursor) - [VS Code](https://composio.dev/toolkits/snowflake/framework/vscode) - [OpenCode](https://composio.dev/toolkits/snowflake/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/snowflake/framework/openclaw) - [Hermes](https://composio.dev/toolkits/snowflake/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/snowflake/framework/cli) - [Google ADK](https://composio.dev/toolkits/snowflake/framework/google-adk) - [LangChain](https://composio.dev/toolkits/snowflake/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/snowflake/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/snowflake/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/snowflake/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/snowflake/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Install the required dependencies for Autogen and Composio - Initialize Composio and create a Tool Router session for Snowflake - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Snowflake tools - Run a live chat loop where you ask the agent to perform Snowflake operations ## What is AutoGen? Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows. Key features include: - Multi-Agent Systems: Build collaborative agent workflows - MCP Workbench: Native support for Model Context Protocol tools - Streaming HTTP: Connect to external services through streamable HTTP - AssistantAgent: Pre-built agent class for tool-using assistants ## 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 | Tool slug | Name | Description | |---|---|---| | `SNOWFLAKE_CANCEL_STATEMENT_EXECUTION` | Cancel Statement Execution | Cancels the execution of a running SQL statement. Use this action to stop a long-running query. | | `SNOWFLAKE_CHECK_STATEMENT_STATUS` | Check Statement Status | Retrieves the status and results of a previously submitted SQL statement using its statement handle. Use this to poll async queries submitted via SNOWFLAKE_SUBMIT_SQL_STATEMENT; call repeatedly until status is no longer pending. Use SNOWFLAKE_CANCEL_STATEMENT to abort a hanging query. | | `SNOWFLAKE_EXECUTE_SQL` | Execute SQL | Execute SQL statements in Snowflake and retrieve results. Supports SELECT queries for data retrieval, DDL statements (CREATE, ALTER, DROP) for schema management, and DML statements (INSERT, UPDATE, DELETE) for data modification. Returns comprehensive result metadata including column types, row counts, and execution status. Unquoted SQL identifiers are auto-uppercased by Snowflake — use matching case in `database`, `schema_name`, `warehouse`, and `role` parameters to avoid 'object not found' errors. Always apply explicit time-range filters and a LIMIT clause to unbounded SELECT queries to prevent large, slow result sets. | | `SNOWFLAKE_FETCH_CATALOG_INTEGRATION` | Fetch Catalog Integration | Retrieves detailed configuration and metadata for a specific catalog integration. Catalog integrations allow Snowflake to connect to external Apache Iceberg catalogs (AWS Glue, Snowflake Open Catalog/Polaris, or Apache Iceberg REST catalogs) to query Iceberg tables managed by those external systems. | | `SNOWFLAKE_GET_ACTIVE_SCHEDULED_MAINTENANCES` | Get Active Scheduled Maintenances | Retrieves a list of any active scheduled maintenances currently in the In Progress or Verifying state. | | `SNOWFLAKE_GET_ALL_SCHEDULED_MAINTENANCES` | Get All Scheduled Maintenances | Retrieves a list of the 50 most recent scheduled maintenances, including those in the Completed state. | | `SNOWFLAKE_GET_COMPONENT_STATUS` | Get Component Status | Retrieves the status of individual components, each listed with its current status. | | `SNOWFLAKE_GET_STATUS_ROLLUP` | Get Status Rollup | Retrieves the status rollup for the entire page, including indicators and human-readable descriptions of the blended component status. | | `SNOWFLAKE_GET_STATUS_SUMMARY` | Get Status Summary | Retrieves the current status summary from Snowflake's public status page (status.snowflake.com). Returns overall system status, operational status of all regional components (AWS, Azure, GCP regions), any unresolved incidents, and upcoming or in-progress scheduled maintenances. This is a public endpoint that provides global Snowflake service status, not account-specific information. | | `SNOWFLAKE_GET_UNRESOLVED_INCIDENTS` | Get Unresolved Incidents | Retrieves a list of any unresolved incidents from the Snowflake status page. This endpoint returns incidents currently in the Investigating, Identified, or Monitoring state. Returns an empty list if there are no active incidents. This is a public status page API that does not require authentication. | | `SNOWFLAKE_GET_UPCOMING_SCHEDULED_MAINTENANCES` | Get Upcoming Scheduled Maintenances | Retrieves upcoming scheduled maintenances from Snowflake's public status page. This action queries the Snowflake status API to get a list of any scheduled maintenance events that are still in the 'Scheduled' state (not yet started or completed). The response includes maintenance details such as impact level, scheduled time windows, incident updates, and direct links to the maintenance notices. Note: This uses Snowflake's public status API and does not require authentication. | | `SNOWFLAKE_SHOW_DATABASES` | Show Databases | Lists all databases for which you have access privileges. Shows database metadata including name, creation date, owner, retention time, and more. Can filter results and include dropped databases within Time Travel retention period. | | `SNOWFLAKE_SHOW_SCHEMAS` | Show Schemas | Lists all schemas for which you have access privileges. Shows schema metadata including name, creation date, owner, database, retention time, and more. Can filter results and include dropped schemas within Time Travel retention period. | | `SNOWFLAKE_SHOW_TABLES` | Show Tables | Lists all tables for which you have access privileges. Shows table metadata including name, creation date, owner, database, schema, row count, size in bytes, clustering keys, and more. Can filter results and include dropped tables within Time Travel retention period. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Snowflake MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Snowflake. Instead of manually wiring Snowflake APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Snowflake account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Snowflake via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables Create a .env file in your project folder. What's happening: - COMPOSIO_API_KEY is required to talk to Composio - OPENAI_API_KEY is used by Autogen's OpenAI client - USER_ID is how Composio identifies which user's Snowflake connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Snowflake tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Snowflake session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["snowflake"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for. What's happening: - url points to the Tool Router MCP endpoint from Composio - timeout is the HTTP timeout for requests - sse_read_timeout controls how long to wait when streaming responses - terminate_on_close=True cleans up the MCP server process when the workbench is closed ```python # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Snowflake tools from the workbench ```python # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Snowflake assistant agent with MCP tools agent = AssistantAgent( name="snowflake_assistant", description="An AI assistant that helps with Snowflake operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Snowflake tools to call via MCP - agent.run_stream(...) yields streaming messages as the agent thinks and calls tools - Typing exit, quit, or bye ends the loop ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Snowflake related question or task to the agent.\n") # Conversation loop while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, "content") and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Snowflake session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["snowflake"] ) url = session.mcp.url # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Snowflake assistant agent with MCP tools agent = AssistantAgent( name="snowflake_assistant", description="An AI assistant that helps with Snowflake operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Snowflake related question or task to the agent.\n") # Conversation loop while True: user_input = input("You: ").strip() if user_input.lower() in ['exit', 'quit', 'bye']: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, 'content') and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You now have an Autogen assistant wired into Snowflake through Composio's Tool Router and MCP. From here you can: - Add more toolkits to the toolkits list, for example notion or hubspot - Refine the agent description to point it at specific workflows - Wrap this script behind a UI, Slack bot, or internal tool Once the pattern is clear for Snowflake, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Snowflake MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/snowflake/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/snowflake/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/snowflake/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/snowflake/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/snowflake/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/snowflake/framework/codex) - [Cursor](https://composio.dev/toolkits/snowflake/framework/cursor) - [VS Code](https://composio.dev/toolkits/snowflake/framework/vscode) - [OpenCode](https://composio.dev/toolkits/snowflake/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/snowflake/framework/openclaw) - [Hermes](https://composio.dev/toolkits/snowflake/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/snowflake/framework/cli) - [Google ADK](https://composio.dev/toolkits/snowflake/framework/google-adk) - [LangChain](https://composio.dev/toolkits/snowflake/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/snowflake/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/snowflake/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/snowflake/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/snowflake/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. - [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly. ## Frequently Asked Questions ### 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 Autogen? Yes, you can. Autogen 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)