mcp-trip-orchestrator
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-trip-orchestratorPlan a 5-day trip to Paris under $1500 including flights, hotel, and activities."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🚀 Built an MCP-Powered Multi-Agent Travel Planner
I’ve been exploring how to design practical Agentic AI systems, so I built MCP Trip Orchestrator using Python, Gemini, LangChain, Tavily MCP, and Pydantic.
The project implements 3 core agentic patterns:
🔹 Supervisor / Orchestrator Coordinates the workflow and manages budget allocation across tasks.
🔹 Multi-Agent Collaboration Specialized workers independently handle: 🚆 Transport 🏨 Accommodation 🏰 Experiences & Dining
🔹 Tool Use + Structured Extraction Agents use Tavily MCP for live web search and Gemini + Pydantic to transform unstructured results into structured booking data.
Architecture
User Request
↓
Supervisor / Orchestrator
├── Transport Agent
├── Stay Agent
└── Experience Agent
↓
Tavily MCP
↓
Gemini
↓
Structured Output
↓
Final Trip PlanWhat I’m focusing on is not just using an LLM, but understanding how to build reliable agentic workflows around LLMs using tools, specialization, orchestration, and structured outputs.
🛠️ Python | Gemini | LangChain | Tavily MCP | Pydantic
Next step: making the orchestrator fully adaptive so it can re-plan when constraints change or a worker fails.
#AgenticAI #AIAgents #MCP #ModelContextProtocol #GenerativeAI #Gemini #LangChain #Python #MultiAgentSystems #AIEngineering #MachineLearning #OpenToWork
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Maintenance
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