n8n is a powerful workflow automation tool that allows you to connect different services and build sophisticated automation. By integrating Membit with n8n, you can enhance your AI agents with real-time social context, enabling them to provide up-to-date insights about trending topics, news, and conversations happening across the web.

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Prerequisites

Before you begin, make sure you have:
  • An active n8n instance (cloud or self-hosted)
  • A Membit account with an API key get your API key
  • Basic familiarity with n8n workflows and AI agents

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Setting Up Membit MCP Client

Follow these steps to integrate Membit with your n8n workflows:
1

Open the Node Panel

In your n8n workflow editor, open the node panel to add a new node to your workflow.
n8n node panel interface showing available nodes
2

Search for MCP Client

In the node search bar, type “MCP client” to find the Model Context Protocol client tool.
Searching for MCP client in n8n node panel
The MCP Client Tool should appear in the search results. This tool enables n8n to communicate with external MCP servers like Membit.
3

Configure Membit MCP Client

Add the MCP Client Tool to your workflow and configure it with the following settings:
Endpoint: https://mcp.membit.ai/mcp
Server Transport: HTTP Streamable
Authentication: Header Auth
  - Name: X-Membit-Api-Key
  - Value: <your-api-key>
Tools to Include: All
Replace <your-api-key> with your actual Membit API key.

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Building Your First Workflow

Now that your Membit MCP client is configured, let’s create a complete workflow that uses real-time context:
1

Add Required Nodes

Create a complete workflow by adding and connecting the following nodes:
  1. Chat Trigger - To receive user input
  2. AI Agent - To process requests and coordinate responses
  3. Google Gemini Chat Model - For natural language processing
  4. Membit MCP Client - For real-time context
2

Connect the Nodes

Connect them in this order:
  • Chat Trigger → AI Agent
  • Google Gemini Chat Model → AI Agent
  • Membit MCP Client → AI Agent
Complete n8n workflow showing connected nodes for Membit integration
The visual connections show the data flow - the AI Agent receives input from the Chat Trigger, uses the Gemini model for processing, and accesses Membit for real-time context.
3

Test Your Workflow

Use the chat interface to send a test message like:
"Tell me about crypto news"
or
"What's trending in AI today?"
Your AI agent should now respond with current, contextual information pulled from Membit’s real-time data feed.
n8n workflow execution result showing AI response with real-time context

Example response showing real-time context from Membit

If successful, you’ll see responses that include recent trends, discussions, and insights that wouldn’t be available in static training data.

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Troubleshooting