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simple chat interfaces and LangGraph with Model Context Protocol (MCP) for custom agent workflows.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EYou'll build two complete applications that demonstrate external access patterns for Snowflake:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApp 1: Chat with GPT-5\u003C/strong\u003E - A Streamlit chat interface that uses the OpenAI SDK to access Snowflake-hosted models like GPT-5 and Claude.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EApp 2: LangGraph MCP Agent\u003C/strong\u003E - A LangGraph agent that connects to Snowflake's MCP server, enabling AI-powered sales intelligence through conversation search, metrics analysis, and data queries.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use the OpenAI SDK to access Snowflake-hosted models (GPT-5, Claude)\u003C/li\u003E\u003Cli\u003EHow to build custom agents with LangGraph and Model Context Protocol\u003C/li\u003E\u003Cli\u003EHow to configure OAuth 2.0 authentication for MCP servers\u003C/li\u003E\u003Cli\u003EHow to create and expose Cortex Agents via MCP\u003C/li\u003E\u003Cli\u003EHow to query structured and unstructured data with AI\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with ACCOUNTADMIN role\u003C/li\u003E\u003Cli\u003EPython 3.11 or higher installed\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL and Python\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Setup\u003C/h2\u003E\n","\u003Ch3\u003EDownload Project Files\u003C/h3\u003E\n","\u003Cp\u003EDownload the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/raw/refs/heads/master/site/sfguides/src/get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents/resources/get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents.zip\"\u003Eget-started-with-openai-sdk-and-managed-mcp-for-cortex-agents.zip\u003C/a\u003E file which contains:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esetup.sql\u003C/code\u003E - Complete Snowflake environment setup\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Emcp_setup.sql\u003C/code\u003E - MCP server and OAuth configuration\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eopenai_sdk.py\u003C/code\u003E - Streamlit chat application using OpenAI SDK\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eagent.py\u003C/code\u003E - LangGraph MCP agent application\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Erequirements.txt\u003C/code\u003E - Python dependencies\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E.env.example\u003C/code\u003E - Environment configuration template\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EExtract the zip file to your preferred location.\u003C/p\u003E\n","\u003Ch3\u003ERun Complete Setup Script\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen a SQL worksheet in Snowsight\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E → \u003Cstrong\u003EWorksheets\u003C/strong\u003E in the left navigation\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Worksheet\u003C/strong\u003E to create a new SQL worksheet\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EOpen the \u003Ccode\u003Esetup.sql\u003C/code\u003E file from the extracted zip folder\u003C/li\u003E\u003Cli\u003ECopy and paste the entire script into the worksheet\u003C/li\u003E\u003Cli\u003ERun all statements by clicking \u003Cstrong\u003ERun All\u003C/strong\u003E at the top right\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis enables \u003Ccode\u003ECORTEX_ENABLED_CROSS_REGION = 'ANY_REGION'\u003C/code\u003E so you can access GPT-5 and all models regardless of your region.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat the setup script does:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E✅ Enable Cortex AI + cross-region inference (GPT-5 access)\u003C/li\u003E\u003Cli\u003E✅ Create database, schema, warehouse\u003C/li\u003E\u003Cli\u003E✅ Create sample sales data (conversations + metrics)\u003C/li\u003E\u003Cli\u003E✅ Create Cortex Search service (for semantic search)\u003C/li\u003E\u003Cli\u003E✅ Create semantic view for Cortex Analyst\u003C/li\u003E\u003Cli\u003E✅ Setup role with permissions\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Cortex Agent via UI\u003C/h2\u003E\n","\u003Ch3\u003EBuild the Sales Intelligence Agent\u003C/h3\u003E\n","\u003Cp\u003EBefore setting up the MCP server, create the Cortex Agent through Snowsight's UI:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAI & ML\u003C/strong\u003E → \u003Cstrong\u003EAgents\u003C/strong\u003E in the left navigation menu\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Agent\u003C/strong\u003E in the top right corner\u003C/li\u003E\u003Cli\u003EConfigure the agent with the following settings:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBasic Information:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EName:\u003C/strong\u003E \u003Ccode\u003ESALES_AGENT\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003ESALES_INTELLIGENCE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EDATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Ccode\u003EAI agent that can search sales conversations, analyze metrics, and query sales data\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdd Tools (in this order):\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETool 1 - Cortex Search:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Tool\u003C/strong\u003E → Select \u003Cstrong\u003ECortex Search\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search Service:\u003C/strong\u003E \u003Ccode\u003ESALES_INTELLIGENCE.DATA.SALES_CONVERSATIONS_SEARCH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Ccode\u003ESearch through sales conversation transcripts\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETool 2 - Cortex Analyst:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Tool\u003C/strong\u003E → Select \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic Model:\u003C/strong\u003E \u003Ccode\u003ESALES_INTELLIGENCE.DATA.SALES_METRICS_SEMANTIC_VIEW\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription:\u003C/strong\u003E \u003Ccode\u003EAnswer questions about sales metrics and performance\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate Agent\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe Cortex Agent combines all three tools into a single intelligent assistant that can search conversations, analyze metrics, and query data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ETest Your Agent\u003C/h3\u003E\n","\u003Cp\u003EBefore proceeding, verify the agent works:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Agent interface, try asking: \u003Ccode\u003E"What are the top performing sales reps?"\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe agent should use Cortex Analyst to query the metrics\u003C/li\u003E\u003Cli\u003ETry: \u003Ccode\u003E"Find conversations about budget concerns"\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe agent should use Cortex Search to find relevant conversations\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup MCP Server with OAuth\u003C/h2\u003E\n","\u003Ch3\u003ECreate OAuth Integration and MCP Server\u003C/h3\u003E\n","\u003Cp\u003ENow that your Cortex Agent is created, set up the MCP server to expose it externally:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen a new SQL worksheet in Snowsight\n\u003Cul\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E → \u003Cstrong\u003EWorksheets\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Worksheet\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EOpen the \u003Ccode\u003Emcp_setup.sql\u003C/code\u003E file from the extracted zip folder\u003C/li\u003E\u003Cli\u003ECopy and paste the entire script into the worksheet\u003C/li\u003E\u003Cli\u003ERun all statements by clicking \u003Cstrong\u003ERun All\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat the MCP setup script does:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E✅ Creates OAuth security integration for authentication\u003C/li\u003E\u003Cli\u003E✅ Generates OAuth client credentials (client ID and secret)\u003C/li\u003E\u003Cli\u003E✅ Creates MCP server that exposes your Cortex Agent\u003C/li\u003E\u003Cli\u003E✅ Grants necessary permissions to the role\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESave OAuth Credentials\u003C/h3\u003E\n","\u003Cp\u003EAfter running the script, you'll see output from \u003Ccode\u003ESELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('SALES_MCP_OAUTH');\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "OAUTH_CLIENT_ID": "abc123...",\n  "OAUTH_CLIENT_SECRET": "xyz789..."\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECRITICAL:\u003C/strong\u003E Copy and save both the \u003Ccode\u003EOAUTH_CLIENT_ID\u003C/code\u003E and \u003Ccode\u003EOAUTH_CLIENT_SECRET\u003C/code\u003E - you'll need these for the agent.py application and cannot retrieve them later!\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate Programmatic Access Token\u003C/h2\u003E\n","\u003Cp\u003EYou'll need a Programmatic Access Token (PAT) for both applications.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, navigate to Profile → Settings → Authentication\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGenerate new token\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ESingle Role\u003C/strong\u003E → \u003Ccode\u003ESALES_INTELLIGENCE_ROLE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECopy the token\u003C/strong\u003E (you won't be able to retrieve it later!)\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EStore this token securely - you'll use it in both applications' configuration.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Python Environment\u003C/h2\u003E\n","\u003Ch3\u003EInstall Dependencies\u003C/h3\u003E\n","\u003Cp\u003ENavigate to the extracted project directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth applications share the same Python environment and configuration:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Environment Variables\u003C/h3\u003E\n","\u003Cp\u003ECreate your environment file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp .env.example .env\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEdit \u003Ccode\u003E.env\u003C/code\u003E and add all your credentials:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-env\"\u003E# Snowflake Account URL (for App 1 - OpenAI SDK)\nSNOWFLAKE_ACCOUNT_URL="https://<account-identifier>.snowflakecomputing.com"\nSNOWFLAKE_PAT=your_token_from_earlier\n\n# Snowflake Connection Details (for App 2 - LangGraph MCP)\nSNOWFLAKE_ACCOUNT=your-account-identifier\nSNOWFLAKE_WAREHOUSE=SALES_INTELLIGENCE_WH\nSNOWFLAKE_DATABASE=SALES_INTELLIGENCE\nSNOWFLAKE_SCHEMA=DATA\nSNOWFLAKE_ROLE=SALES_INTELLIGENCE_ROLE\n\n# OAuth Credentials (for App 2 - from SYSTEM$SHOW_OAUTH_CLIENT_SECRETS)\nOAUTH_CLIENT_ID="Your Client ID Here"\nOAUTH_CLIENT_SECRET="Your Client Secret Here"\n\n# MCP Server Name (for App 2)\nMCP_SERVER_NAME=SALES_INTELLIGENCE_MCP\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis single \u003Ccode\u003E.env\u003C/code\u003E file contains configuration for both applications.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EApp 1: Chat with GPT-5 (openai_sdk.py)\u003C/h2\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA simple chat interface that uses the OpenAI SDK to access Snowflake-hosted models.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EArchitecture:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EStreamlit App (openai_sdk.py) → OpenAI SDK → Snowflake Cortex API → GPT-5/Claude\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the App\u003C/h3\u003E\n","\u003Cp\u003EStart the Streamlit application:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Estreamlit run openai_sdk.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe app will open at \u003Ccode\u003Ehttp://localhost:8501\u003C/code\u003E\u003C/p\u003E\n","\u003Ch3\u003ETest the Application\u003C/h3\u003E\n","\u003Cp\u003ETry these sample queries:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat is Snowflake Cortex?\n\nWrite a Python function for calculating Fibonacci numbers\n\nGenerate SQL to find top 5 customers by revenue\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAvailable Models\u003C/h3\u003E\n","\u003Cp\u003ESelect from the dropdown menu:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Eopenai-gpt-5\u003C/code\u003E - GPT-5 (cutting edge)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eopenai-gpt-5-mini\u003C/code\u003E - Faster GPT-5 variant\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eopenai-gpt-5-nano\u003C/code\u003E - Most efficient GPT-5 variant\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eopenai-gpt-5-chat\u003C/code\u003E - GPT-5 optimized for chat\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eopenai-gpt-4-1\u003C/code\u003E - GPT-4.1\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eclaude-sonnet-4-5\u003C/code\u003E - Anthropic Claude Sonnet 4.5\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eclaude-4-sonnet\u003C/code\u003E - Claude 4 Sonnet\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESuccess!\u003C/strong\u003E You're using GPT-5 hosted in Snowflake via the standard OpenAI SDK.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EApp 2: LangGraph MCP Agent (agent.py)\u003C/h2\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA LangGraph agent that connects to Snowflake's Cortex Agent via Model Context Protocol (MCP) with OAuth 2.0 authentication.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EArchitecture:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EYour LangGraph Agent (agent.py - MCP Client)\n    ↓ OAuth 2.0 + MCP Protocol\nSnowflake MCP Server\n    ↓\nCortex Agent (SALES_AGENT)\n    ↓\n├─ Cortex Search (semantic search over conversations)\n├─ Cortex Analyst (natural language to SQL)\n└─ SQL Execution (direct queries)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUnderstanding the Application\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Eagent.py\u003C/code\u003E application demonstrates how to build a custom LangGraph agent that connects to Snowflake's MCP server. Key features:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. OAuth 2.0 Authentication Flow:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOpens browser for user authorization\u003C/li\u003E\u003Cli\u003EStarts local callback server on port 3000\u003C/li\u003E\u003Cli\u003EExchanges authorization code for access token\u003C/li\u003E\u003Cli\u003EHandles token refresh automatically\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. MCP Protocol Integration:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInitializes MCP session with Snowflake\u003C/li\u003E\u003Cli\u003EDiscovers available tools from Cortex Agent\u003C/li\u003E\u003Cli\u003ESends JSON-RPC requests to MCP server\u003C/li\u003E\u003Cli\u003EMaintains session state across interactions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. LangGraph Workflow:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUses Snowflake Cortex (GPT-5) as the reasoning LLM\u003C/li\u003E\u003Cli\u003EMaintains conversation history and context\u003C/li\u003E\u003Cli\u003EOrchestrates tool calls based on user queries\u003C/li\u003E\u003Cli\u003EProvides structured responses with source attribution\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Interactive Chat Interface:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECommand-line based conversation interface\u003C/li\u003E\u003Cli\u003EBuilt-in test mode for tool verification\u003C/li\u003E\u003Cli\u003EReal-time streaming of agent responses\u003C/li\u003E\u003Cli\u003EError handling and troubleshooting\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe agent uses Snowflake Cortex (GPT-5) as its LLM, keeping all computation within Snowflake.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERun the Agent\u003C/h3\u003E\n","\u003Cp\u003EStart the interactive agent:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython agent.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYour agent will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen browser for OAuth 2.0 authorization\u003C/li\u003E\u003Cli\u003EConnect to Snowflake's MCP server\u003C/li\u003E\u003Cli\u003EDiscover available Cortex Agent tools\u003C/li\u003E\u003Cli\u003EInitialize LangGraph workflow with Snowflake Cortex LLM\u003C/li\u003E\u003Cli\u003EStart interactive session\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ETest Queries\u003C/h3\u003E\n","\u003Cp\u003ETry these sample queries:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESearch conversations:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EFind conversations about budget concerns\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery metrics:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat is the total deal value by product line?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECombined analysis:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWho are the top performing sales reps and what conversations did they have?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDirect SQL:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EShow me all closed deals\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESuccess!\u003C/strong\u003E You built a custom agent with OAuth 2.0 authentication using LangGraph and MCP.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003ECongratulations! You've successfully built two applications that access Snowflake externally using different approaches. The \u003Ccode\u003Eopenai_sdk.py\u003C/code\u003E application demonstrated using the OpenAI SDK for simple LLM inference, while the \u003Ccode\u003Eagent.py\u003C/code\u003E application showed how to connect your LangGraph agent to Snowflake's Cortex Agent via Model Context Protocol with OAuth 2.0 authentication.\u003C/p\u003E\n","\u003Cp\u003EThese patterns enable you to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELeverage Snowflake-hosted AI models from any application\u003C/li\u003E\u003Cli\u003EConnect custom agents to Snowflake's Cortex Agent capabilities\u003C/li\u003E\u003Cli\u003ESecure external access with OAuth 2.0 authentication\u003C/li\u003E\u003Cli\u003ECombine semantic search, natural language to SQL, and direct queries\u003C/li\u003E\u003Cli\u003EAccess models like GPT-5 through familiar APIs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to configure Snowflake for external AI model access\u003C/li\u003E\u003Cli\u003EHow to use the OpenAI SDK with Snowflake-hosted models\u003C/li\u003E\u003Cli\u003EHow to set up OAuth 2.0 authentication for MCP servers\u003C/li\u003E\u003Cli\u003EHow to create and expose Cortex Agents via MCP\u003C/li\u003E\u003Cli\u003EHow to build LangGraph agents using Model Context Protocol\u003C/li\u003E\u003Cli\u003EHow to implement semantic search over unstructured data\u003C/li\u003E\u003Cli\u003EHow to enable natural language to SQL translation with Cortex Analyst\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDocumentation:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"hhttps://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql\"\u003ESnowflake Cortex AI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp\"\u003EMCP Servers in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://langchain-ai.github.io/langgraph/\"\u003ELangGraph Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://modelcontextprotocol.io\"\u003EModel Context Protocol\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Connect OpenAI SDK to Snowflake Cortex Agents via Managed MCP for AI assistant development and data-driven chatbots.","title":"Get Started with OpenAI SDK and Managed MCP for Cortex Agents","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n\n### Overview\n\nLearn how to access Snowflake-hosted AI models and data from external applications using two different approaches: the OpenAI SDK for simple chat interfaces and LangGraph with Model Context Protocol (MCP) for custom agent workflows.\n\n### What You Will Build\n\nYou'll build two complete applications that demonstrate external access patterns for Snowflake:\n\n**App 1: Chat with GPT-5** - A Streamlit chat interface that uses the OpenAI SDK to access Snowflake-hosted models like GPT-5 and Claude.\n\n**App 2: LangGraph MCP Agent** - A LangGraph agent that connects to Snowflake's MCP server, enabling AI-powered sales intelligence through conversation search, metrics analysis, and data queries.\n\n### What You Will Learn\n\n- How to use the OpenAI SDK to access Snowflake-hosted models (GPT-5, Claude)\n- How to build custom agents with LangGraph and Model Context Protocol\n- How to configure OAuth 2.0 authentication for MCP servers\n- How to create and expose Cortex Agents via MCP\n- How to query structured and unstructured data with AI\n\n### Prerequisites\n\n- [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with ACCOUNTADMIN role\n- Python 3.11 or higher installed\n- Basic knowledge of SQL and Python\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Setup\n\n### Download Project Files\n\nDownload the [get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents.zip](https://github.com/Snowflake-Labs/sfquickstarts/raw/refs/heads/master/site/sfguides/src/get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents/resources/get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents.zip) file which contains:\n- `setup.sql` - Complete Snowflake environment setup\n- `mcp_setup.sql` - MCP server and OAuth configuration\n- `openai_sdk.py` - Streamlit chat application using OpenAI SDK\n- `agent.py` - LangGraph MCP agent application\n- `requirements.txt` - Python dependencies\n- `.env.example` - Environment configuration template\n\nExtract the zip file to your preferred location.\n\n### Run Complete Setup Script\n\n1. Open a SQL worksheet in Snowsight\n   * Navigate to **Projects** → **Worksheets** in the left navigation\n   * Click **+ Worksheet** to create a new SQL worksheet\n2. Open the `setup.sql` file from the extracted zip folder\n3. Copy and paste the entire script into the worksheet\n4. Run all statements by clicking **Run All** at the top right\n\n\u003E This enables `CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION'` so you can access GPT-5 and all models regardless of your region.\n\n**What the setup script does:**\n- ✅ Enable Cortex AI + cross-region inference (GPT-5 access)\n- ✅ Create database, schema, warehouse\n- ✅ Create sample sales data (conversations + metrics)\n- ✅ Create Cortex Search service (for semantic search)\n- ✅ Create semantic view for Cortex Analyst\n- ✅ Setup role with permissions\n\n\u003C!-- ------------------------ --\u003E\n## Create Cortex Agent via UI\n\n\n### Build the Sales Intelligence Agent\n\nBefore setting up the MCP server, create the Cortex Agent through Snowsight's UI:\n\n1. Navigate to **AI & ML** → **Agents** in the left navigation menu\n2. Click **+ Agent** in the top right corner\n3. Configure the agent with the following settings:\n\n**Basic Information:**\n- **Name:** `SALES_AGENT`\n- **Database:** `SALES_INTELLIGENCE`\n- **Schema:** `DATA`\n- **Description:** `AI agent that can search sales conversations, analyze metrics, and query sales data`\n\n**Add Tools (in this order):**\n\n**Tool 1 - Cortex Search:**\n- Click **+ Tool** → Select **Cortex Search**\n- **Cortex Search Service:** `SALES_INTELLIGENCE.DATA.SALES_CONVERSATIONS_SEARCH`\n- **Description:** `Search through sales conversation transcripts`\n\n**Tool 2 - Cortex Analyst:**\n- Click **+ Tool** → Select **Cortex Analyst**  \n- **Semantic Model:** `SALES_INTELLIGENCE.DATA.SALES_METRICS_SEMANTIC_VIEW`\n- **Description:** `Answer questions about sales metrics and performance`\n\n4. Click **Create Agent**\n\n\u003E The Cortex Agent combines all three tools into a single intelligent assistant that can search conversations, analyze metrics, and query data.\n\n### Test Your Agent\n\nBefore proceeding, verify the agent works:\n\n1. In the Agent interface, try asking: `\"What are the top performing sales reps?\"`\n2. The agent should use Cortex Analyst to query the metrics\n3. Try: `\"Find conversations about budget concerns\"`\n4. The agent should use Cortex Search to find relevant conversations\n\n\u003C!-- ------------------------ --\u003E\n## Setup MCP Server with OAuth\n\n### Create OAuth Integration and MCP Server\n\nNow that your Cortex Agent is created, set up the MCP server to expose it externally:\n\n1. Open a new SQL worksheet in Snowsight\n   * Navigate to **Projects** → **Worksheets**\n   * Click **+ Worksheet**\n2. Open the `mcp_setup.sql` file from the extracted zip folder\n3. Copy and paste the entire script into the worksheet\n4. Run all statements by clicking **Run All**\n\n**What the MCP setup script does:**\n- ✅ Creates OAuth security integration for authentication\n- ✅ Generates OAuth client credentials (client ID and secret)\n- ✅ Creates MCP server that exposes your Cortex Agent\n- ✅ Grants necessary permissions to the role\n\n### Save OAuth Credentials\n\nAfter running the script, you'll see output from `SELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('SALES_MCP_OAUTH');`:\n\n```json\n{\n  \"OAUTH_CLIENT_ID\": \"abc123...\",\n  \"OAUTH_CLIENT_SECRET\": \"xyz789...\"\n}\n```\n\n\u003E **CRITICAL:** Copy and save both the `OAUTH_CLIENT_ID` and `OAUTH_CLIENT_SECRET` - you'll need these for the agent.py application and cannot retrieve them later!\n\n\u003C!-- ------------------------ --\u003E\n## Generate Programmatic Access Token\n\nYou'll need a Programmatic Access Token (PAT) for both applications.\n\n1. In Snowsight, navigate to Profile → Settings → Authentication\n2. Click **Generate new token**\n3. Select **Single Role** → `SALES_INTELLIGENCE_ROLE`\n4. **Copy the token** (you won't be able to retrieve it later!)\n\n\u003E Store this token securely - you'll use it in both applications' configuration.\n\n\u003C!-- ------------------------ --\u003E\n## Setup Python Environment\n\n\n### Install Dependencies\n\nNavigate to the extracted project directory:\n\n```bash\ncd get-started-with-openai-sdk-and-managed-mcp-for-cortex-agents\n```\n\nBoth applications share the same Python environment and configuration:\n\n```bash\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Configure Environment Variables\n\nCreate your environment file:\n\n```bash\ncp .env.example .env\n```\n\nEdit `.env` and add all your credentials:\n\n```env\n# Snowflake Account URL (for App 1 - OpenAI SDK)\nSNOWFLAKE_ACCOUNT_URL=\"https://\u003Caccount-identifier\u003E.snowflakecomputing.com\"\nSNOWFLAKE_PAT=your_token_from_earlier\n\n# Snowflake Connection Details (for App 2 - LangGraph MCP)\nSNOWFLAKE_ACCOUNT=your-account-identifier\nSNOWFLAKE_WAREHOUSE=SALES_INTELLIGENCE_WH\nSNOWFLAKE_DATABASE=SALES_INTELLIGENCE\nSNOWFLAKE_SCHEMA=DATA\nSNOWFLAKE_ROLE=SALES_INTELLIGENCE_ROLE\n\n# OAuth Credentials (for App 2 - from SYSTEM$SHOW_OAUTH_CLIENT_SECRETS)\nOAUTH_CLIENT_ID=\"Your Client ID Here\"\nOAUTH_CLIENT_SECRET=\"Your Client Secret Here\"\n\n# MCP Server Name (for App 2)\nMCP_SERVER_NAME=SALES_INTELLIGENCE_MCP\n```\n\n\u003E This single `.env` file contains configuration for both applications.\n\n\u003C!-- ------------------------ --\u003E\n## App 1: Chat with GPT-5 (openai_sdk.py)\n\n\n### What You'll Build\n\nA simple chat interface that uses the OpenAI SDK to access Snowflake-hosted models.\n\n**Architecture:**\n```\nStreamlit App (openai_sdk.py) → OpenAI SDK → Snowflake Cortex API → GPT-5/Claude\n```\n\n### Run the App\n\nStart the Streamlit application:\n\n```bash\nstreamlit run openai_sdk.py\n```\n\nThe app will open at `http://localhost:8501`\n\n### Test the Application\n\nTry these sample queries:\n\n```\nWhat is Snowflake Cortex?\n\nWrite a Python function for calculating Fibonacci numbers\n\nGenerate SQL to find top 5 customers by revenue\n```\n\n### Available Models\n\nSelect from the dropdown menu:\n- `openai-gpt-5` - GPT-5 (cutting edge)\n- `openai-gpt-5-mini` - Faster GPT-5 variant\n- `openai-gpt-5-nano` - Most efficient GPT-5 variant\n- `openai-gpt-5-chat` - GPT-5 optimized for chat\n- `openai-gpt-4-1` - GPT-4.1\n- `claude-sonnet-4-5` - Anthropic Claude Sonnet 4.5\n- `claude-4-sonnet` - Claude 4 Sonnet\n\n\u003E **Success!** You're using GPT-5 hosted in Snowflake via the standard OpenAI SDK.\n\n\u003C!-- ------------------------ --\u003E\n## App 2: LangGraph MCP Agent (agent.py)\n\n\n### What You'll Build\n\nA LangGraph agent that connects to Snowflake's Cortex Agent via Model Context Protocol (MCP) with OAuth 2.0 authentication.\n\n**Architecture:**\n```\nYour LangGraph Agent (agent.py - MCP Client)\n    ↓ OAuth 2.0 + MCP Protocol\nSnowflake MCP Server\n    ↓\nCortex Agent (SALES_AGENT)\n    ↓\n├─ Cortex Search (semantic search over conversations)\n├─ Cortex Analyst (natural language to SQL)\n└─ SQL Execution (direct queries)\n```\n\n### Understanding the Application\n\nThe `agent.py` application demonstrates how to build a custom LangGraph agent that connects to Snowflake's MCP server. Key features:\n\n**1. OAuth 2.0 Authentication Flow:**\n- Opens browser for user authorization\n- Starts local callback server on port 3000\n- Exchanges authorization code for access token\n- Handles token refresh automatically\n\n**2. MCP Protocol Integration:**\n- Initializes MCP session with Snowflake\n- Discovers available tools from Cortex Agent\n- Sends JSON-RPC requests to MCP server\n- Maintains session state across interactions\n\n**3. LangGraph Workflow:**\n- Uses Snowflake Cortex (GPT-5) as the reasoning LLM\n- Maintains conversation history and context\n- Orchestrates tool calls based on user queries\n- Provides structured responses with source attribution\n\n**4. Interactive Chat Interface:**\n- Command-line based conversation interface\n- Built-in test mode for tool verification\n- Real-time streaming of agent responses\n- Error handling and troubleshooting\n\n\u003E The agent uses Snowflake Cortex (GPT-5) as its LLM, keeping all computation within Snowflake.\n\n### Run the Agent\n\nStart the interactive agent:\n\n```bash\npython agent.py\n```\n\nYour agent will:\n1. Open browser for OAuth 2.0 authorization\n2. Connect to Snowflake's MCP server\n3. Discover available Cortex Agent tools\n4. Initialize LangGraph workflow with Snowflake Cortex LLM\n5. Start interactive session\n\n### Test Queries\n\nTry these sample queries:\n\n**Search conversations:**\n```\nFind conversations about budget concerns\n```\n\n**Query metrics:**\n```\nWhat is the total deal value by product line?\n```\n\n**Combined analysis:**\n```\nWho are the top performing sales reps and what conversations did they have?\n```\n\n**Direct SQL:**\n```\nShow me all closed deals\n```\n\n\u003E **Success!** You built a custom agent with OAuth 2.0 authentication using LangGraph and MCP.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\n\n### Conclusion\n\nCongratulations! You've successfully built two applications that access Snowflake externally using different approaches. The `openai_sdk.py` application demonstrated using the OpenAI SDK for simple LLM inference, while the `agent.py` application showed how to connect your LangGraph agent to Snowflake's Cortex Agent via Model Context Protocol with OAuth 2.0 authentication.\n\nThese patterns enable you to:\n- Leverage Snowflake-hosted AI models from any application\n- Connect custom agents to Snowflake's Cortex Agent capabilities\n- Secure external access with OAuth 2.0 authentication\n- Combine semantic search, natural language to SQL, and direct queries\n- Access models like GPT-5 through familiar APIs\n\n### What You Learned\n\n- How to configure Snowflake for external AI model access\n- How to use the OpenAI SDK with Snowflake-hosted models\n- How to set up OAuth 2.0 authentication for MCP servers\n- How to create and expose Cortex Agents via MCP\n- How to build LangGraph agents using Model Context Protocol\n- How to implement semantic search over unstructured data\n- How to enable natural language to SQL translation with Cortex Analyst\n\n### Resources\n\n**Documentation:**\n- [Snowflake Cortex AI](hhttps://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql)\n- [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [MCP Servers in Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp)\n- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)\n- [Model Context Protocol](https://modelcontextprotocol.io)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-86cd6c9520","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-81ce3cef6d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-43bf8bf95a","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b2c4578643","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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