Getting Started

Checklist

You'll complete the following to successfully launch your MCP implementation:

• Deploy the MCP onboarding Template

• Construct a Flow

• Deploy Application

• Set up MCP Client

Step 1: Construct a flow

Begin by identifying the tasks your conversational AI application will automate and organize them into individual topics handled by flows. Determine the sequence of steps and messaging that the conversational application follows to assist a user with the task. The conversation workflow is assembled in a flow's Canvas with a pattern of nodes similar to a flow diagram.

Each flow is invoked when your chosen AI model identifies customer intent from a user's query ("Where can I get Portuguese food in Atlanta?") and matches it to a flow you've created (MCPFoodRecommendation).

Variables required for the flow to work that should be extracted by the LLM in conversation and passed along to NLX are set up first in the flow's Settings:

• Select Flows in workspace menu > Choose New flow > Enter a descriptive name (no spaces or special characters) > Select Save

• Choose Settings (gear icon) in flow toolbar

• From the AI settings tab:

  • In the AI description field, enter a concise description explaining the purpose of the flow that LLM models reference to invoke the flow (e.g., A tool for providing food recommendations based on cuisine and location)

  • Enable MCP toggle

    • Provide a unique and concise input name (no spaces or special characters)

    • Enter the input schema containing necessary variable(s) that will be set and passed along by the LLM interfacing with a user

    • Expand each property variable defined in your MCP input schema > Expand their settings

      • Enter a brief description in the property's Description field for the LLM to understand the purpose and context of each (e.g., cuisine property might have the accompanying description for a weather update flow: The cuisine for the recommendation)

• Click Save

On any node of the flow, enter an open curly brace { and reference the MCP input variable you want to use as an output in messaging, payload fields, Split node conditions, etc:

Step 2: Deploy application

Now you'll set up and deploy an application to become your MCP server.

• Select Applications from workspace menu > Choose New application

• Enter a descriptive name > Click Save

• Click Flows tab of application > Select Attach flows > Attach one or more flows created to make available to your application > Click Attach selected

• Select Channels tab of application > Expand API option > Click Edit channel

  • Enable MCP interface toggle

  • Click Update channel

• Select Settings tab of application > Under AI settings, enter a concise description of the application's purpose into the AI description field

• Click Save

A build constructs the array of flows that make up your conversational AI application and updates any changes made to your flows, while deploying makes a successful build live:

• Click Deployment tab of application > Select Create or Review & build

• Wait for validation to complete > Select Create build*

• When satisfied with a successful build, click Deploy

*After a build status appears as 🟢 Built, you may use the Test feature to test the conversation with your application using the latest build.

🧠 Looking for more? See Manage channels​

Step 3: Set up MCP Client

To make your application available to a supported MCP Client, complete the following:

• From the Deployment tab of your NLX application, select Details next to the Deployed status

• Expand the MCP section under Setup instructions in the pop-up

• Select the MCP Client you wish to use > Complete the MCP setup instructions listed

{
  "mcpServers": {
    "YourApplicationName": {
      "command": "npx",
      "args": [
        "-y",
        "@nlxai/mcp-nodejs-server"
      ],
      "env": {
        "NLX_API_KEY": "your API key",
        "NLX_APP_URL": "your MCP URL"
      }
    }
  }
}

{
  "mcpServers": {
    "YourApplicationName": {
      "command": "npx",
      "args": [
        "-y",
        "@nlxai/mcp-nodejs-server"
      ],
      "env": {
        "NLX_API_KEY": "your API key",
        "NLX_APP_URL": "your MCP URL"
      }
    }
  }
}

{
  "mcpServers": {
    "YourApplicationName": {
      "command": "npx",
      "args": [
        "-y",
        "@nlxai/mcp-nodejs-server"
      ],
      "env": {
        "NLX_API_KEY": "your API key",
        "NLX_APP_URL": "your MCP URL"
      }
    }
  }
}