OrgFlow MCP

import dotenv from 'dotenv'; import { GoogleGenerativeAI } from '@google/generative-ai'; import { Client } from '@modelcontextprotocol/sdk/client/index.js'; import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'; import readline from 'readline'; import path from 'path'; dotenv.config(); const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!); const model = genAI.getGenerativeModel({ model: 'gemini-2.5-pro', // Updated to Gemini 2.5 Pro generationConfig: { temperature: 0.7, maxOutputTokens: 2048, topK: 40, topP: 0.95, } }); async function main() { const client = new Client({ name: 'org-client', version: '1.0.0' }); const transport = new StdioClientTransport({ command: process.execPath, args: [path.resolve(process.cwd(), 'dist/server/index.js')], env: process.env as Record<string, string> }); await client.connect(transport); console.log('Connected to server!'); const rl = readline.createInterface({ input: process.stdin, output: process.stdout }); while (true) { const userInput = await new Promise<string>(resolve => rl.question('\nYou: ', resolve)); if (userInput.toLowerCase() === 'exit') break; const tools = await client.listTools(); const functionDeclarations = tools.tools.map(t => ({ name: t.name, description: t.description || '', parameters: t.inputSchema as any })); const chat = model.startChat({ tools: [{ functionDeclarations }] }); const result = await chat.sendMessage(userInput); const response = result.response; const functionCalls = response.functionCalls(); if (functionCalls && functionCalls.length > 0) { for (const fc of functionCalls) { const toolResult = await client.callTool({ name: fc.name, arguments: (fc.args || {}) as Record<string, unknown> }); console.log('\nResult:', JSON.stringify(toolResult.content, null, 2)); } } else { console.log('\nAssistant:', response.text()); } } await client.close(); process.exit(0); } main().catch(console.error);