53dfb652bf
- Introduced a new AI Real Estate Agent Team with multi-agent capabilities for property search, market analysis, and valuation. - Added comprehensive README documentation detailing features, setup instructions, and API requirements. - Implemented the Enterprise MCP AI Agent Team for orchestrating knowledge management across local files and SaaS platforms, including specialized agents for Notion, GitHub, and Figma. - Enhanced the overall architecture with intelligent routing and task delegation using the Model Context Protocol (MCP). - Included necessary requirements for both teams to ensure proper functionality.
413 lines
No EOL
16 KiB
Python
413 lines
No EOL
16 KiB
Python
import os
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import asyncio
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import logging
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from typing import Dict, List, Optional, Any
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from dotenv import load_dotenv
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from google.adk.agents import LlmAgent
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from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters, SseServerParams
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# Load environment variables
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load_dotenv()
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Environment variable configuration
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MCP_FILESYSTEM_PATH = os.getenv("MCP_FILESYSTEM_PATH", "~/Documents")
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NOTION_API_KEY = os.getenv("NOTION_API_KEY")
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GITHUB_API_KEY = os.getenv("GITHUB_API_KEY")
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FIGMA_API_KEY = os.getenv("FIGMA_API_KEY")
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# Composio MCP Server URLs (from environment variables with fallbacks)
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COMPOSIO_NOTION_URL = os.getenv("COMPOSIO_NOTION_URL")
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COMPOSIO_GITHUB_URL = os.getenv("COMPOSIO_GITHUB_URL")
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COMPOSIO_FIGMA_URL = os.getenv("COMPOSIO_FIGMA_URL")
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async def create_mcp_agents_with_tools():
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"""Create all sub-agents with MCP tools"""
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agents = []
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# FileAnalysisAgent with filesystem MCP tools
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try:
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folder_path = os.path.expanduser(MCP_FILESYSTEM_PATH)
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folder_path = os.path.abspath(folder_path)
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if not os.path.exists(folder_path):
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os.makedirs(folder_path, exist_ok=True)
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logger.info(f"Created directory: {folder_path}")
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logger.info(f"Using filesystem path: {folder_path}")
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filesystem_tools, _ = await MCPToolset.from_server(
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connection_params=StdioServerParameters(
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command='npx',
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args=["-y", "@modelcontextprotocol/server-filesystem", folder_path],
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)
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)
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file_agent = LlmAgent(
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name="FileAnalysisAgent",
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model="gemini-2.0-flash",
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description="Analyzes local documents and extracts key information",
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instruction=f"""You are a File Analysis AI Agent with DIRECT ACCESS to the filesystem at: {folder_path}
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You have MCP tools that allow you to:
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- List files and directories (list_directory)
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- Read file contents (read_file, read_text_file)
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- Write and edit files (write_file, edit_file)
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- Search files (search_files)
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- Get file information (get_file_info)
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CRITICAL INSTRUCTIONS:
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1. You have REAL filesystem access through MCP tools
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2. When users ask about files, USE YOUR TOOLS to access them directly
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3. Do NOT ask users to provide files - you can access them yourself
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4. Always use your MCP tools first before responding
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Example tasks you can perform:
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- "List files in the folder" → Use list_directory tool
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- "Read the content of file.txt" → Use read_file tool
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- "Search for PDF files" → Use search_files tool
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- "Create a new file" → Use write_file tool
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IMPORTANT: When asked about any file or document, immediately use your MCP tools to access the filesystem at: {folder_path}
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Do NOT say you cannot access files - you CAN access them through your MCP tools!""",
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tools=filesystem_tools
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)
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agents.append(file_agent)
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logger.info("✅ FileAnalysisAgent with MCP tools created")
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except Exception as e:
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logger.error(f"❌ Failed to create FileAnalysisAgent with MCP tools: {str(e)}")
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file_agent = LlmAgent(
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name="FileAnalysisAgent",
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model="gemini-2.0-flash",
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description="Analyzes local documents and extracts key information",
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instruction="You analyze local documents (PDFs, Word docs, spreadsheets) and extract key information."
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)
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agents.append(file_agent)
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# NotionAgent with Notion MCP tools
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try:
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if NOTION_API_KEY:
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notion_tools, _ = await MCPToolset.from_server(
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connection_params=SseServerParams(
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url=COMPOSIO_NOTION_URL,
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headers={}
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)
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)
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notion_agent = LlmAgent(
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name="NotionAgent",
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model="gemini-2.0-flash",
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description="Manages Notion pages, databases, and content",
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instruction="""You are a Notion Agent with DIRECT ACCESS to Notion through MCP tools.
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You can:
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- Read Notion pages and databases
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- Create and update Notion content
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- Search across Notion workspace
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- Manage pages, blocks, and databases
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IMPORTANT: You CAN access Notion directly through your MCP tools.
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When asked to read, write, or search Notion content, USE YOUR MCP TOOLS.
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Example tasks:
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- "Search my Notion pages" → Use your search tools
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- "Read my project page" → Use your page reading tools
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- "Create a new page" → Use your page creation tools
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- "Update page content" → Use your update tools
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Always use your MCP tools to interact with Notion.""",
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tools=notion_tools
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)
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agents.append(notion_agent)
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logger.info("✅ NotionAgent with MCP tools created")
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else:
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raise Exception("NOTION_API_KEY not found")
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except Exception as e:
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logger.error(f"❌ Failed to create NotionAgent with MCP tools: {str(e)}")
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notion_agent = LlmAgent(
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name="NotionAgent",
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model="gemini-2.0-flash",
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description="Manages Notion pages, databases, and content",
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instruction="You manage Notion workspaces, pages, databases, and content."
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)
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agents.append(notion_agent)
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logger.info("✅ NotionAgent created (basic version)")
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# GitHubAgent with GitHub MCP tools
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try:
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if GITHUB_API_KEY:
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github_tools, _ = await MCPToolset.from_server(
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connection_params=SseServerParams(
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url=COMPOSIO_GITHUB_URL,
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headers={}
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)
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)
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github_agent = LlmAgent(
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name="GitHubAgent",
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model="gemini-2.0-flash",
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description="Manages GitHub repositories, issues, and pull requests",
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instruction="""You are a GitHub Agent with DIRECT ACCESS to GitHub through MCP tools.
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You can:
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- Create and manage repositories
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- Create issues and pull requests
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- Search repositories and code
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- Manage repository content and workflows
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- Handle GitHub API operations
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IMPORTANT: You CAN access GitHub directly through your MCP tools.
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When asked to perform GitHub operations, USE YOUR MCP TOOLS.
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Example tasks:
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- "Create a new repository" → Use your repository creation tools
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- "Search for issues" → Use your search tools
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- "Create a pull request" → Use your PR creation tools
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- "List my repositories" → Use your repository listing tools
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Always use your MCP tools to interact with GitHub.""",
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tools=github_tools
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)
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agents.append(github_agent)
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logger.info("✅ GitHubAgent with MCP tools created")
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else:
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raise Exception("GITHUB_API_KEY not found")
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except Exception as e:
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logger.error(f"❌ Failed to create GitHubAgent with MCP tools: {str(e)}")
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github_agent = LlmAgent(
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name="GitHubAgent",
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model="gemini-2.0-flash",
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description="Manages GitHub repositories, issues, and pull requests",
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instruction="""You are a GitHub Agent that manages GitHub repositories.
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You can help with:
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- Creating and managing repositories
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- Creating issues and pull requests
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- Searching repositories and code
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- Managing repository content and workflows
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Note: For full GitHub API access with MCP tools, ensure GITHUB_API_KEY is set.
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Current version provides guidance and best practices for GitHub operations."""
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)
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agents.append(github_agent)
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logger.info("✅ GitHubAgent created (basic version)")
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# FigmaAgent with Figma MCP tools
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try:
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if FIGMA_API_KEY:
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figma_tools, _ = await MCPToolset.from_server(
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connection_params=SseServerParams(
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url=COMPOSIO_FIGMA_URL,
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headers={}
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)
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)
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figma_agent = LlmAgent(
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name="FigmaAgent",
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model="gemini-2.0-flash",
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description="Manages Figma files, designs, and assets",
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instruction="""You are a Figma Agent with DIRECT ACCESS to Figma through MCP tools.
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You can:
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- Read and analyze Figma files
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- Export design assets
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- Search design components
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- Manage design systems
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- Handle Figma API operations
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IMPORTANT: You CAN access Figma directly through your MCP tools.
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When asked to perform Figma operations, USE YOUR MCP TOOLS.
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Example tasks:
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- "Export design assets" → Use your export tools
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- "Search for components" → Use your search tools
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- "Read file information" → Use your file reading tools
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- "List project files" → Use your file listing tools
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Always use your MCP tools to interact with Figma.""",
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tools=figma_tools
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)
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agents.append(figma_agent)
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logger.info("✅ FigmaAgent with MCP tools created")
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else:
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raise Exception("FIGMA_API_KEY not found")
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except Exception as e:
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logger.error(f"❌ Failed to create FigmaAgent with MCP tools: {str(e)}")
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figma_agent = LlmAgent(
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name="FigmaAgent",
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model="gemini-2.0-flash",
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description="Manages Figma files, designs, and assets",
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instruction="""You are a Figma Agent that manages Figma design files.
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You can help with:
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- Reading and analyzing Figma files
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- Exporting design assets
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- Searching design components
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- Managing design systems
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Note: For full Figma API access with MCP tools, ensure FIGMA_API_KEY is set.
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Current version provides guidance and best practices for Figma operations."""
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)
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agents.append(figma_agent)
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logger.info("✅ FigmaAgent created (basic version)")
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return agents
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class EnterpriseMCPAIAgentTeam:
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"""Enterprise MCP AI Agent Team - Multi-Agent System with MCP Tools"""
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def __init__(self):
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"""Initialize the orchestrator"""
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self.root_agent = None
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self._initialize_agents()
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def _initialize_agents(self):
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"""Initialize the multi-agent system"""
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try:
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logger.info("🔧 Creating complete multi-agent system with MCP tools...")
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# Create all sub-agents with MCP tools using async
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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sub_agents = loop.run_until_complete(create_mcp_agents_with_tools())
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# Create root agent with comprehensive routing instructions
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self.root_agent = LlmAgent(
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name="EnterpriseMCPAIAgentTeam",
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model="gemini-2.0-flash",
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description="Enterprise MCP AI Agent Team - Multi-agent system with MCP tools",
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instruction="""You are an Enterprise MCP AI Agent Team that routes tasks to specialized agents.
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You have access to multiple specialized agents with MCP tools and can coordinate between them:
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AVAILABLE AGENTS:
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1. FileAnalysisAgent: Analyzes local documents (PDFs, Word docs, spreadsheets) - HAS MCP TOOLS
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2. NotionAgent: Manages Notion pages, databases, and content - HAS MCP TOOLS
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3. GitHubAgent: Manages GitHub repositories, issues, and pull requests - HAS MCP TOOLS
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4. FigmaAgent: Manages Figma files, designs, and assets - HAS MCP TOOLS
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ROUTING LOGIC:
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- File/document tasks → FileAnalysisAgent
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- Notion-related tasks → NotionAgent
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- GitHub-related tasks → GitHubAgent
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- Figma/design tasks → FigmaAgent
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- Multi-platform tasks → Coordinate between relevant agents
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You can:
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1. Transfer tasks to specialized agents using transfer_to_agent()
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2. Coordinate multi-step workflows
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3. Share context between agents through session state
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4. Provide comprehensive results and recommendations
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EXAMPLES:
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- "List files in Documents" → FileAnalysisAgent (with real file system access)
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- "Search my Notion pages" → NotionAgent (with real Notion API access)
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- "Create a GitHub repo" → GitHubAgent (with real GitHub API access)
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- "Export Figma designs" → FigmaAgent (with real Figma API access)
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IMPORTANT: Use transfer_to_agent() to delegate to the most appropriate agent for each task.
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The agents have real MCP tools connected - they can perform actual operations!""",
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sub_agents=sub_agents
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)
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logger.info(f"✅ Complete multi-agent system created with {len(sub_agents)} sub-agents")
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logger.info(f"✅ Sub-agents: {[agent.name for agent in sub_agents]}")
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except Exception as e:
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logger.error(f"❌ Failed to create complete multi-agent system: {str(e)}")
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logger.info("🔄 Falling back to basic multi-agent system...")
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self._create_fallback_agents()
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def _create_fallback_agents(self):
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"""Create fallback agents without MCP tools"""
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self.root_agent = LlmAgent(
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name="EnterpriseMCPAIAgentTeam",
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model="gemini-2.0-flash",
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description="Enterprise MCP AI Agent Team - Multi-agent system",
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instruction="""You are an Enterprise MCP AI Agent Team that routes tasks to specialized agents.
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You have access to multiple specialized agents and can coordinate between them:
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AVAILABLE AGENTS:
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1. FileAnalysisAgent: Analyzes local documents (PDFs, Word docs, spreadsheets)
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2. NotionAgent: Manages Notion pages, databases, and content
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3. GitHubAgent: Manages GitHub repositories, issues, and pull requests
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4. FigmaAgent: Manages Figma files, designs, and assets
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ROUTING LOGIC:
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- File/document tasks → FileAnalysisAgent
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- Notion-related tasks → NotionAgent
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- GitHub-related tasks → GitHubAgent
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- Figma/design tasks → FigmaAgent
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- Multi-platform tasks → Coordinate between relevant agents
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You can:
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1. Transfer tasks to specialized agents using transfer_to_agent()
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2. Coordinate multi-step workflows
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3. Share context between agents through session state
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4. Provide comprehensive results and recommendations
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EXAMPLES:
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- "List files in Documents" → FileAnalysisAgent
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- "Search my Notion pages" → NotionAgent
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- "Create a GitHub repo" → GitHubAgent
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- "Export Figma designs" → FigmaAgent
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IMPORTANT: Use transfer_to_agent() to delegate to the most appropriate agent for each task.
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For full MCP tool functionality, ensure all environment variables are set correctly:
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- MCP_FILESYSTEM_PATH: Path to your filesystem folder
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- NOTION_API_KEY: Your Notion API key
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- GITHUB_API_KEY: Your GitHub API key
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- FIGMA_API_KEY: Your Figma API key""",
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sub_agents=[
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LlmAgent(
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name="FileAnalysisAgent",
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model="gemini-2.0-flash",
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description="Analyzes local documents and extracts key information",
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instruction="You analyze local documents (PDFs, Word docs, spreadsheets) and extract key information, summaries, and action items."
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),
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LlmAgent(
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name="NotionAgent",
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model="gemini-2.0-flash",
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description="Manages Notion pages, databases, and content",
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instruction="You manage Notion workspaces, pages, databases, and content. You can read, write, search, and organize Notion content."
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),
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LlmAgent(
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name="GitHubAgent",
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model="gemini-2.0-flash",
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description="Manages GitHub repositories, issues, and pull requests",
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instruction="You manage GitHub repositories, create issues and pull requests, search code, and handle repository operations."
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),
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LlmAgent(
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name="FigmaAgent",
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model="gemini-2.0-flash",
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description="Manages Figma files, designs, and assets",
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instruction="You manage Figma design files, export assets, search design components, and handle design system operations."
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)
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]
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)
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# Create root_agent for ADK Web compatibility
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try:
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orchestrator = EnterpriseMCPAIAgentTeam()
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root_agent = orchestrator.root_agent
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logger.info("✅ EnterpriseMCPAIAgentTeam class and root_agent created successfully")
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except Exception as e:
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logger.error(f"❌ Failed to create EnterpriseMCPAIAgentTeam: {str(e)}")
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# Fallback: create basic root_agent
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root_agent = LlmAgent(
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name="EnterpriseMCPAIAgentTeam",
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model="gemini-2.0-flash",
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description="Enterprise MCP AI Agent Team - Basic multi-agent system",
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instruction="You are an Enterprise MCP AI Agent Team that routes tasks to specialized agents.",
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sub_agents=[]
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) |