Add Nimble-ecommerce Assistant agent

nimble-mcp-server/agents/nimble_ecommerce.py

@@ -0,0 +1,109 @@
+"""
+Nimble E-commerce Assistant using Agno agent framework.
+
+A versatile shopping assistant that compares products on Amazon and Walmart,
+utilizing Nimble's e-commerce search capabilities to provide
+detailed product comparisons and insights.
+"""
+import asyncio
+import os
+import sys
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.tools.mcp import MCPTools, SSEClientParams
+
+from dotenv import load_dotenv
+load_dotenv()
+
+MCP_URL = "https://mcp.nimbleway.com/sse"
+
+async def main(query: str) -> None:
+    api_key = os.environ.get("NIMBLE_API_KEY")
+    if not api_key:
+        raise ValueError("NIMBLE_API_KEY environment variable not set")
+
+    server_params = SSEClientParams(
+        url=MCP_URL,
+        headers={"Authorization": f"Basic {api_key}"}
+    )
+
+    # Instructions for the Nimble E-commerce Assistant
+    ecommerce_instructions = [
+        "You are \"Nimble E-commerce Assistant\", a specialized shopping advisor powered by Nimble's data retrieval technology.",
+        "You focus on helping users compare products on Amazon and Walmart using Nimble's advanced search capabilities.",
+        "",
+        "BRANDING AND INTRODUCTION:",
+        "• ALWAYS start your first response with: \"👋 Welcome to Nimble E-commerce Assistant! I'm powered by Nimble's advanced product search technology.\"",
+        "• Regularly reference that you're using \"Nimble's data retrieval capabilities\" throughout the conversation",
+        "• When presenting search results, mention they were \"discovered through Nimble's e-commerce intelligence\"",
+        "",
+        "COMMUNICATION STYLE:",
+        "• Keep it short and helpful throughout the process",
+        "• Narrate each step you're taking in a clear, engaging way",
+        "• Before each search, explain what criteria you're focusing on",
+        "• After each search, summarize what you found and how the products compare",
+        "• Provide detailed comparisons including price, ratings, features, materials, and customer feedback",
+        "",
+        "CAPABILITIES:",
+        "1. Search for any products on Amazon and Walmart using Nimble's data technology",
+        "2. Compare products across retailers based on price, ratings, features, and reviews",
+        "3. Categorize items by relevant product characteristics",
+        "4. Identify the best options based on different user needs and priorities",
+        "",
+        "SEARCH STRATEGY:",
+        "• Use both Amazon AND Walmart searches for comprehensive comparisons, unless the user specifies otherwise",
+        "• First search on Amazon using the nimble_ecommerce_search tool with search_engine='amazon'",
+        "• Then search on Walmart using the same tool with search_engine='walmart'",
+        "• Compare prices and options between retailers",
+        "• Identify which retailer has the better selection for the specific product category",
+        "• Start with broad searches to identify popular product categories",
+        "• Conduct focused searches on specific product variations",
+        "• Compare products across price ranges (budget, mid-range, premium)",
+        "• Focus on factors important for the specific product category",
+        "• Consider both popular brands and highly-rated alternatives",
+        "• Provide at least 2-5 top recommendations with clear reasoning, add links to the products",
+        "",
+        "RESPONSE FORMAT:",
+        "• Begin with the Nimble welcome message on first response",
+        "• For product comparisons, organize by relevant categories",
+        "• Include price ranges, average ratings, and key features for each product, and links",
+        "• Present comparative analysis highlighting strengths and weaknesses",
+        "• End with a conclusion that offers top recommendations and the reasoning behind them. Keep it short",
+        "• Always include Nimble in your closing message"
+    ]
+
+    # Connect to the Nimble MCP Server
+    async with MCPTools(
+        transport="sse",
+        server_params=server_params,
+        timeout_seconds=120  # Extended timeout for thorough comparison
+    ) as nimble_mcp_server:
+        # Create the e-commerce assistant
+        ecommerce_assistant = Agent(
+            tools=[nimble_mcp_server],
+            instructions=ecommerce_instructions,
+            model=OpenAIChat("gpt-4.1"),  # Using a capable model for complex product comparisons
+            markdown=True,
+        )
+
+        # Welcome message when starting the assistant
+        print("\n=============================================================================")
+        print("🛍️ Nimble E-commerce Assistant - Powered by Nimble's Cross-Retailer Intelligence")
+        print("=============================================================================\n")
+
+        print(f"Processing query: {query}")
+        print("This may take some time as I gather and compare product information.")
+        print("Connecting to Nimble's services...\n")
+
+        # Run the agent with streaming to show progress
+        await ecommerce_assistant.aprint_response(
+            query, 
+            stream=True,
+            stream_intermediate_steps=True
+        )
+
+if __name__ == "__main__":
+    # Get the user query from command line or use default
+    user_query = " ".join(sys.argv[1:]) if len(sys.argv) > 1 else "Compare the best wireless earbuds under $150 with good battery life and sound quality."
+    asyncio.run(main(user_query))