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The MCP workflow is an n8n AI agent with a chat. Its tools are the callable blueprints of one application: the agent connects to the application’s MCP address with your evaluation key, and a chat model decides which blueprint to call. Use it when people should ask questions in a chat and get answers that come from your blueprints.

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Before you start

  • The application is deployed and has an evaluation key (Deploy a release and call it, Create and revoke an evaluation key).
  • You can sign in to the Admin UI, and you hold content:read in the environment, for example as an auditor. Without it, the n8n card says “You need the content:read right”.
  • An n8n instance that can reach the runtime cluster. The steps were run with n8n 2.44.5; other versions may name the menu items differently.
  • An account with a chat model provider that n8n supports, such as OpenAI or Anthropic, and its credentials.

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Steps

1

Download the workflow

Open the Admin UI (the cluster address followed by /runtime-management/ui/), click Sign in with Leapter and sign in with the account you use for the Lab. The Admin UI may open on another environment: click the environment’s name at the bottom of the sidebar and choose yours in the list that opens. Then click the application’s name on the Status page, and scroll down to the n8n card. Under MCP, click MCP workflow. Your browser saves a file such as pizza-pricing-calculator-180269-playground-mcp.json.
The Admin UI application page, scrolled to the n8n card. Under MCP, the row One agent workflow for the whole application has the button MCP workflow with a green ring. Under Blueprints, Pizza Pricing has the buttons HTTP workflow and Code workflow.

The MCP workflow button: one workflow for the whole application.

2

Import it into n8n

Sign in to n8n and create a new workflow: click + at the top of the left sidebar, then New workflow. Open the workflow menu (
 next to the workflow name), choose Import, then From file, and select the downloaded file.The workflow is renamed to “MCP Agent” followed by the name the project has in the Lab, for example “MCP Agent - Pizza Pricing Calculator”. You should see four nodes. When chat message received leads to AI Agent. Below AI Agent hang Simple Memory (its memory) and MCP Client (its tools). AI Agent and MCP Client show a red warning sign: the agent has no chat model yet, and the MCP Client has no key yet.
3

Add your key to the MCP Client

Double-click the node MCP Client. Endpoint already holds the application’s MCP address, Server Transport is HTTP Streamable and Authentication is Header Auth.Under Credential for Header Auth, click Connect to Header Auth. In the dialog Header Auth account, fill in:Click Save. n8n says “Credential successfully created inside your personal space”, and Credential for Header Auth shows Header Auth account.If your n8n already has a Header Auth credential, n8n fills it in instead of showing Connect to Header Auth. Unless that credential holds an evaluation key of this application, open the list under Credential for Header Auth, choose Create new credential and fill in the dialog as above.
4

Check the connection

In the open node MCP Client, click Execute step. The dialog Test MCP Client opens. Click the Tool name list: it shows one tool per callable blueprint of the application, here pizza_pricing. n8n read this list from the runtime cluster with your key.
The n8n dialog Test MCP Client, with the text Provide the data that would normally come from the AI Agent node. The Tool name list is open and shows one entry, pizza_pricing, with a green ring.

The Tool name list shows the blueprint pizza_pricing as a tool.

Close the dialog with × without choosing a tool, and close the node with × at its top right. On the canvas, MCP Client has no warning sign any more. AI Agent keeps its warning until it has a chat model.
5

Connect a chat model

On the canvas, click + under Chat Model on the node AI Agent. n8n opens the list Language Models. Choose the node of your provider, for example Anthropic Chat Model or OpenAI Chat Model, and create its credential with your provider’s key. n8n’s page for each node describes its credential, for example OpenAI Chat Model.
The imported MCP workflow in n8n. When chat message received leads to AI Agent, which has a red warning sign. Under AI Agent, the connector Chat Model with a red star and a plus button has a green ring; Memory leads to Simple Memory and Tool leads to MCP Client.

The AI Agent still needs a chat model: Chat Model has a red star until one is connected.

6

Chat with the agent

Click Open chat below the canvas. Ask a question that a blueprint answers, for example “What does a large pizza with thin crust, mushroom and olive cost on a Friday?”.

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Result

The agent has the application’s blueprints as tools and can call pizza_pricing through the MCP Client to answer in the chat. The chat model decides which tool to call and how to word the answer. Each tool call is a run of the release, recorded like any other call (Find a run and read usage). The MCP address names no release. The agent always reaches the release the environment serves at the time of the call, so after a deploy or a roll back you do not need to download the workflow again.

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Troubleshooting

The dialog Test MCP Client says “Could not connect to your MCP server. Authentication failed.” The value of the credential is not an evaluation key of this application, or the key is revoked. Open the credential with the pencil next to Credential for Header Auth, enter the right key in Value and click Save. After you choose pizza_pricing under Tool name, the dialog Test MCP Client says “Cannot read properties of undefined (reading ‘inputType’)”. n8n 2.44.5 shows this after a tool is chosen in that dialog. The check only needs the list: close the dialog with ×. After Sign in with Leapter, you are in the Lab and not in the Admin UI, or the Lab says “This sign-in cannot go on.” and “authorization not found”. Open the Admin UI’s address again and click Sign in with Leapter once more. The Status page opens.

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What to do next