Integrate LibreChat with Live SAP SuccessFactors Data via CData Connect AI

LibreChat is an open-source, self-hosted AI chat platform that brings together multiple LLM providers, agents, and assistants behind a single interface. It also supports the Model Context Protocol (MCP), so you can connect external tools and data sources directly to the chat and pull in live data from the systems you already work with.

By integrating LibreChat with CData Connect AI through the built-in MCP Server, LibreChat gains governed, real-time access to live SAP SuccessFactors data. This enables you to list catalogs, explore schemas, and query records from SAP SuccessFactors data using natural language prompts, with all data access running securely against authorized sources.

This article explains how to configure SAP SuccessFactors connectivity in Connect AI, generate the required personal access token, install LibreChat, register the Connect AI MCP Server, configure an LLM provider, and verify the integration by querying live SAP SuccessFactors data from the LibreChat interface.

Step 1: Configure SAP SuccessFactors connectivity for LibreChat

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

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

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

3. Enter the necessary authentication properties to connect to SAP SuccessFactors.

   You can authenticate to SAP Success Factors using Basic authentication or OAuth with SAML assertion.

   Basic Authentication

   You must provide values for the following properties to successfully authenticate to SAP Success Factors. Note that the provider will reuse the session opened by SAP Success Factors using cookies. Which means that your credentials will be used only on the first request to open the session. After that, cookies returned from SAP Success Factors will be used for authentication.

   • Url: set this to the URL of the server hosting Success Factors. Some of the servers are listed in the SAP support documentation (external link).
   • User: set this to the username of your account.
   • Password: set this to the password of your account.
   • CompanyId: set this to the unique identifier of your company.

   OAuth Authentication

   You must provide values for the following properties, which will be used to get the access token.

   • Url: set this to the URL of the server hosting Success Factors. Some of the servers are listed in the SAP support documentation (external link).
   • User: set this to the username of your account.
   • CompanyId: set this to the unique identifier of your company.
   • OAuthClientId: set this to the API Key that was generated in API Center.
   • OAuthClientSecret: the X.509 private key used to sign SAML assertion. The private key can be found in the certificate you downloaded in Registering your OAuth Client Application.
   • InitiateOAuth: set this to GETANDREFRESH.
   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Navigate to the Permissions tab and update user-based permissions
👁 Updating permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from LibreChat. 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 SAP SuccessFactors connection configured and a PAT generated, LibreChat can now connect to SAP SuccessFactors data through Connect AI.

Step 2: Install LibreChat and configure Connect AI MCP

Next, install LibreChat locally and configure the Connect AI Remote MCP Server so that the chat interface can discover and call live data tools through Connect AI.

1. Install LibreChat by following the official installation guide. If you are using the npm setup, make sure MongoDB and MeiliSearch are installed and running locally
2. Once the installation is complete, start LibreChat and open http://localhost:3080/ in your browser to access the chat interface 👁 LibreChat chat interface
   
3. In the left navigation bar, click the MCP Settings icon, then click Add MCP 👁 Adding a new MCP server in LibreChat
   
4. In the Add MCP panel, configure the server with the following values:
   • Name: CData MCP, or any name of your choice
   • Description: Optional description for the server
   • MCP Server URL: https://mcp.cloud.cdata.com/mcp
   • Transport: Streamable HTTPS
   • Authentication: API Key
   • Header Format: Basic
   • API Key: base64-encoded value of email:PAT

   Note: LibreChat will use Basic authentication with Connect AI. Combine your Connect AI user email and the PAT you created earlier in the format email:PAT, then base64 encode the combined string and paste it in the API Key field. For example, [email protected]:ABC123...XYZ789 base64-encoded becomes something like: dXNlckBkb21haW4uY29tOkFCQzEyMy4uLlhZWjc4OQ==

   👁 Configuring the Connect AI MCP Server
   
5. Check I trust this application and click Add to save the server
6. The CData MCP server now appears in the left navigation bar. Click the connect icon next to it to establish the connection to Connect AI 👁 CData MCP server connected in LibreChat
   

Enable the MCP server and configure an LLM provider

LibreChat requires at least one LLM provider to power the chat. Enable the MCP server in the chat input and add an API key for your preferred provider so the model can interpret prompts and call MCP tools through Connect AI.

1. In the chat interface, click the MCP selector at the bottom of the input box and confirm that CData MCP is checked so the tools are exposed to the chat 👁 Enabling the CData MCP server in the chat input
   
2. At the top of the chat, click the model selector and choose your preferred LLM provider (e.g., OpenAI, Anthropic, Google) and model 👁 Selecting an LLM provider and model
   
3. Click Set API Key next to the chosen provider, paste your provider API key, and click Submit 👁 Setting the LLM provider API key
   

With the MCP server and an LLM provider configured, LibreChat is ready to query live SAP SuccessFactors data through Connect AI.

Step 3: Query live SAP SuccessFactors data from LibreChat

With the integration complete, use the LibreChat chat input to interact with live SAP SuccessFactors data through natural language prompts handled by the configured LLM.

1. With the CData MCP server enabled and a model selected, type a prompt in the chat input, for example:
   • List all catalogs in my cdata mcp
   • Show the available schemas and tables for SAP SuccessFactors
   • Query the top 5 records from a table in SAP SuccessFactors data
2. LibreChat calls the Connect AI MCP Server and returns live results from SAP SuccessFactors data 👁 Querying live data from LibreChat
   

At this point, your LibreChat instance communicates with the Connect AI MCP Server and retrieves live SAP SuccessFactors data through remote MCP tools directly from the chat interface.

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