> ## Documentation Index > Fetch the complete documentation index at: https://pipedream.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Pipedream MCP with Gemini > Use Pipedream MCP with Google Gemini Make sure to complete the [setup steps](/connect/mcp/developers#prerequisites) before continuing. Check out the Gemini Agent built by the Gemini team, featured [here](https://x.com/_philschmid/status/1948025573403492621) ## Installation ```bash JavaScript theme={null} npm install @google/generativeai @modelcontextprotocol/sdk @pipedream/sdk ``` ```bash Python theme={null} pip install google-genai fastmcp pipedream ``` Set your Google API key: ```ini theme={null} GOOGLE_API_KEY=your_google_api_key ``` ## Basic Usage ```typescript TypeScript theme={null} import { GoogleGenerativeAI } from '@google/generativeai'; import { Client } from '@modelcontextprotocol/sdk/client/index.js'; import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js'; import { PipedreamClient } from '@pipedream/sdk'; // Environment variables const GOOGLE_API_KEY = process.env.GOOGLE_API_KEY!; const PIPEDREAM_CLIENT_ID = process.env.PIPEDREAM_CLIENT_ID!; const PIPEDREAM_CLIENT_SECRET = process.env.PIPEDREAM_CLIENT_SECRET!; const PIPEDREAM_PROJECT_ID = process.env.PIPEDREAM_PROJECT_ID!; const PIPEDREAM_ENVIRONMENT = process.env.PIPEDREAM_ENVIRONMENT as 'development' | 'production'; async function runGeminiWithMCP() { // Initialize Pipedream SDK client const pdClient = new PipedreamClient({ projectEnvironment: PIPEDREAM_ENVIRONMENT, clientId: PIPEDREAM_CLIENT_ID, clientSecret: PIPEDREAM_CLIENT_SECRET, projectId: PIPEDREAM_PROJECT_ID }); // Get access token for MCP authentication const accessToken = await pdClient.rawAccessToken; const externalUserId = 'user-123'; // Your user's unique ID const appSlug = 'gmail,google_calendar'; // Apps to use // Create MCP transport const transport = new StreamableHTTPClientTransport( new URL('https://remote.mcp.pipedream.net/v3'), { requestInit: { headers: { 'Authorization': `Bearer ${accessToken}`, 'x-pd-project-id': PIPEDREAM_PROJECT_ID, 'x-pd-environment': PIPEDREAM_ENVIRONMENT, 'x-pd-external-user-id': externalUserId, 'x-pd-app-slug': appSlug, } } } ); // Initialize MCP client const mcpClient = new Client({ name: 'gemini-pipedream-client', version: '1.0.0', }); await mcpClient.connect(transport); // Initialize Gemini client const genAI = new GoogleGenerativeAI(GOOGLE_API_KEY); try { // Get available tools from MCP const toolsResponse = await mcpClient.listTools(); const mcpTools = toolsResponse.tools || []; // Convert MCP tools to Gemini function calling format const geminiTools = mcpTools.map((tool: any) => ({ function_declarations: [{ name: tool.name, description: tool.description, parameters: tool.inputSchema, }] })); // Create model with tools const model = genAI.getGenerativeModel({ model: 'gemini-pro', tools: geminiTools, systemInstruction: `You are a helpful AI assistant with access to powerful tools. The current date is ${new Date().toISOString().split('T')[0]}. Use the available tools to help users accomplish their tasks effectively. If you encounter any errors, explain what happened and suggest alternatives.` }); // Start chat const chat = model.startChat({ history: [], }); const userMessage = "Check my recent emails and summarize the important ones"; console.log(`User: ${userMessage}`); // Send message and handle tool calls const result = await chat.sendMessage(userMessage); const response = await result.response; // Handle function calls if present if (response.functionCalls?.length > 0) { console.log('Executing tools...'); const functionResults = []; for (const functionCall of response.functionCalls) { try { const result = await mcpClient.callTool({ name: functionCall.name, arguments: functionCall.args, }); functionResults.push({ name: functionCall.name, response: result, }); console.log(`✅ ${functionCall.name}: Success`); } catch (error) { console.error(`❌ ${functionCall.name}: ${error}`); functionResults.push({ name: functionCall.name, response: { error: String(error) }, }); } } // Send function results back to Gemini const finalResult = await chat.sendMessage([{ functionResponse: { name: functionResults[0].name, response: functionResults[0].response, } }]); console.log(`Gemini: ${finalResult.response.text()}`); } else { console.log(`Gemini: ${response.text()}`); } } finally { // Clean up MCP client await mcpClient.close(); } } // Run the example runGeminiWithMCP().catch(console.error); ``` ```python Python theme={null} import os import asyncio from datetime import datetime from google import genai from fastmcp import Client from pipedream import Pipedream async def run_gemini_with_mcp(): # Initialize Pipedream client and get access token pd = Pipedream( project_id=os.environ.get('PIPEDREAM_PROJECT_ID'), project_environment=os.environ.get('PIPEDREAM_ENVIRONMENT'), client_id=os.environ.get('PIPEDREAM_CLIENT_ID'), client_secret=os.environ.get('PIPEDREAM_CLIENT_SECRET'), ) access_token = await pd.raw_access_token # Initialize Gemini client client = genai.Client(api_key=os.environ.get('GOOGLE_API_KEY')) # Initialize MCP client with Pipedream mcp_client = Client({ "mcpServers": { "pipedream": { "transport": "http", "url": "https://remote.mcp.pipedream.net/v3", "headers": { "Authorization": f"Bearer {access_token}", "x-pd-project-id": os.environ.get('PIPEDREAM_PROJECT_ID'), "x-pd-environment": os.environ.get('PIPEDREAM_ENVIRONMENT'), "x-pd-external-user-id": "user-123", # Your user's unique ID "x-pd-app-slug": "gmail, google_calendar", # Apps to use }, } } }) async with mcp_client: # Configure Gemini with MCP tools and system instructions config = genai.types.GenerateContentConfig( temperature=0, tools=[mcp_client.session], system_instruction=f"""You are a helpful AI assistant with access to powerful tools. Very important: The user's timezone is {datetime.now().strftime("%Z")}. The current date is {datetime.now().strftime("%Y-%m-%d")}. Use the available tools to help users accomplish their tasks effectively. If you encounter any errors, explain what happened and suggest alternatives.""", ) # Create chat session chat = client.aio.chats.create(model="gemini-2.0-flash-exp", config=config) # Send user message user_message = "Check my recent emails and summarize the important ones" print(f"User: {user_message}") # Generate response with automatic tool calling response = await chat.send_message(user_message) print(f"Gemini: {response.text}") # Continue conversation if needed follow_up = "Can you schedule a meeting based on the most important email?" print(f"User: {follow_up}") response = await chat.send_message(follow_up) print(f"Gemini: {response.text}") if __name__ == "__main__": asyncio.run(run_gemini_with_mcp()) ```