Dynamic MCP Server Selection with OpenAI GPT-4 and Contextual AI Reranker

This n8n workflow demonstrates an advanced AI-driven system for dynamically selecting the most appropriate "MCP" (presumably a server or service) based on user input, leveraging OpenAI's GPT-4 for initial understanding and a contextual AI reranker for refined selection. It provides a robust framework for intelligent routing and decision-making within a service-oriented architecture.

What it does

This workflow orchestrates a sophisticated AI interaction to determine the best MCP server:

1. Listens for Chat Messages: The workflow is triggered by an incoming chat message, acting as the initial user query or request.
2. Initial AI Agent Processing: An AI Agent (likely configured with OpenAI GPT-4) processes the incoming chat message. This agent is designed to understand the user's intent and potentially extract key information relevant to MCP server selection.
3. Dynamic HTTP Request (Placeholder): A generic HTTP Request node is included, which in a complete implementation would likely fetch available MCP server details, configurations, or other relevant data needed for the selection process. This could involve querying a database, an API, or a configuration management system.
4. Conditional Logic for Reranking: An "If" node introduces conditional logic. This is where the workflow would decide whether a reranking step is necessary based on the initial AI agent's output or the retrieved MCP server data. For instance, if the initial selection is ambiguous or if multiple suitable MCPs are identified, reranking would be triggered.
5. Code-based Reranking (Contextual AI Reranker): A "Code" node is used to implement the contextual AI reranker. This JavaScript code would take the initial AI output and the MCP server data (from the HTTP Request) and apply a more sophisticated, context-aware algorithm to select the optimal MCP. This could involve comparing embeddings, applying custom scoring, or using another AI model for fine-grained decision-making.
6. Merge Results: A "Merge" node combines the outputs from different branches of the conditional logic, ensuring a unified data stream for the final response.
7. Respond to Chat: The workflow concludes by sending a chat message back to the user, indicating the selected MCP server or providing further instructions based on the AI's decision.
8. Sticky Note for Documentation: A sticky note provides additional context or instructions within the workflow itself.

Prerequisites/Requirements

• n8n Instance: A running n8n instance (self-hosted or cloud).
• OpenAI API Key: Required for the OpenAI Chat Model and AI Agent nodes to function.
• LangChain Integration: The workflow utilizes n8n's LangChain nodes, which may require specific configurations or dependencies within your n8n setup.
• MCP Server Information: Access to a system (via API, database, etc.) that can provide details about available MCP servers. The HTTP Request node serves as a placeholder for this integration.

Setup/Usage

1. Import the Workflow: Import the provided JSON into your n8n instance.
2. Configure Credentials:
   • OpenAI Chat Model: Configure your OpenAI API key in the credentials section for the "OpenAI Chat Model" node.
   • AI Agent: Ensure the "AI Agent" node is correctly configured with your OpenAI credentials and any specific model settings (e.g., GPT-4).
3. Customize HTTP Request: Update the "HTTP Request" node (Node 19) to connect to your actual MCP server information source. This might involve setting the URL, headers, authentication, and request body to fetch relevant data.
4. Refine AI Agent Prompt: Adjust the prompt and configuration of the "AI Agent" node (Node 1119) to accurately interpret user intent for MCP selection.
5. Implement Reranker Logic:
   • If Node: Configure the conditions in the "If" node (Node 20) to determine when the contextual reranker should be invoked.
   • Code Node: Modify the JavaScript code in the "Code" node (Node 834) to implement your specific contextual AI reranking logic. This code will receive the output from the AI Agent and the HTTP Request, and should return the chosen MCP server.
6. Configure Chat Trigger and Response:
   • Chat Trigger: Ensure the "When chat message received" trigger (Node 1247) is correctly set up to listen for messages from your desired chat platform (e.g., Telegram, Slack, custom webhook).
   • Chat Response: Customize the "Chat" node (Node 1313) to formulate the response message back to the user, including the selected MCP server.
7. Activate the Workflow: Save and activate the workflow.

Once configured, the workflow will automatically process incoming chat messages, intelligently select an MCP server, and respond to the user with the decision.