How to Connect Flowise AI Agents to Live FHIR Data via CData Connect AI

Flowise AI is an open-source, no-code tool for building AI workflows and custom agents visually. Its drag-and-drop interface allows you to integrate large language models (LLMs) with APIs, databases, and external systems effortlessly.

CData Connect AI enables real-time connectivity to hundreds of enterprise data sources. Through its Model Context Protocol (MCP) server, CData Connect AI bridges Flowise agents with live FHIR securely and efficiently, no data replication required. By combining Flowise AI's intuitive agent builder with CData's MCP integration, users can create agents capable of fetching, analyzing, and acting upon live FHIR data directly within Flowise AI workflows.

This guide shows you how to connect Flowise AI to CData Connect AI MCP, set up credentials, and enable your agents to query live FHIR data in real time.

Step 1: Configure FHIR Connectivity for Flowise

Connectivity to FHIR from Flowise AI is made possible through CData Connect AI's Remote MCP Server. To interact with FHIR data from Flowise AI, we start by creating and configuring a FHIR connection in CData Connect AI.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a connection in Connect AI

2. Select FHIR from the Add Connection panel
👁 Selecting data source

3. Enter the necessary authentication properties to connect to FHIR.

   Set URL to the Service Base URL of the FHIR server. This is the address where the resources are defined in the FHIR server you would like to connect to. Set ConnectionType to a supported connection type. Set ContentType to the format of your documents. Set AuthScheme based on the authentication requirements for your FHIR server.

   Generic, Azure-based, AWS-based, and Google-based FHIR server implementations are supported.

   Sample Service Base URLs

   • Generic: http://my_fhir_server/r4b/
   • Azure: https://MY_AZURE_FHIR.azurehealthcareapis.com/
   • AWS: https://healthlake.REGION.amazonaws.com/datastore/DATASTORE_ID/r4/
   • Google: https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID/fhir/

   Generic FHIR Instances

   The product supports connections to custom instances of FHIR. Authentication to custom FHIR servers is handled via OAuth (read more about OAuth in the Help documentation. Before you can connect to custom FHIR instances, you must set ConnectionType to Generic.

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab and update user-based permissions
👁 Updating permissions

Once the connection is established, FHIR data is now accessible in CData Connect AI and ready to be used with MCP enabled tools.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Flowise AI. It is best practice to create a separate PAT for each integration to maintain granular access control.

1. Click the gear icon () at the top right of the Connect AI app to open Settings
2. On the Settings page, go to the Access Tokens section and click Create PAT
3. Give the PAT a descriptive name and click Create
👁 Creating a new PAT

4. Copy the token when displayed and store it securely. It will not be shown again

With the FHIR connection configured and a PAT generated, Flowise AI can now connect to FHIR data through Connect AI.

Step 2: Configure Connect AI credentials in Flowise AI

Log in to Flowise AI workspace to set up the integration.

Add OpenAI credentials

1. Navigate to Credentials and choose Add Credential
👁 Click Add credential

2. Select OpenAI API from the dropdown
3. Provide a name (e.g., OpenAI_Key) and paste the API key
👁 Adding OpenAI credentials in Flowise

Add the PAT variable

1. Navigate to Variables and Add Variable
👁 Navigate to Variables

2. Set Variable Name (e.g., PAT), choose Static as type, and set the Value to Base64-encoded username:PAT
3. Click Add to save the variable
👁 Adding PAT as variable in Flowise

Step 3: Build the agent in Flowise AI

1. Go to Agent Flows, select Add New
👁 Agent Flow page

2. Click the "+" icon to add a new node and choose Agent and drag the agent to the workflow
👁 Adding agent node in Flowise

3. Connect the Start node to the Agent node
👁 Connecting agent nodes in Flowise

Configure agent settings

Double-click on the Agent node and fill in the details:

• Model: select ChatOpenAI or preferred model (e.g., gpt-4o-mini)
• Connect Credential: Select OpenAI API key credential which was created earlier
• Streaming: Enabled

👁 Fill in the details for Agent Node

Add the custom MCP tool

1. Under Tools, click Add Tool and choose Custom MCP
2. Fill in the JSON parameters as shown below:

 
{
 "url": "https://mcp.cloud.cdata.com/mcp",
 "headers": {
 "Authorization": "Basic {{$vars.PAT}}"
 }
}

👁 Configuring custom MCP tool in Flowise

Click the refresh icon to load available MCP actions. Once actions are listed, now Flowise agent is successfully connected to CData Connect AI MCP.

Step 4: Test and query live FHIR data in Flowise

1. Open the Chat tab in Flowise
2. Type a query such as "Show top 10 records from FHIR data table"
3. Observe that responses are fetched in real time via the CData Connect AI MCP connection
👁 Testing live queries in Flowise

With the workflow run completed, Flowise demonstrates successful retrieval of Salesforce data through the CData Connect AI MCP server, with the MCP Client node providing the ability to ask questions, retrieve records, and perform actions on the data.

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