Build Agents in Relevance AI with Access to Live Elasticsearch Data via CData Connect AI
Relevance AI is an AI automation and agent-building platform that enables organizations to create autonomous workflows powered by natural language reasoning. Users can visually design agents that interact with APIs, databases, and third-party systems to complete everyday business tasks or data operations.
By integrating Relevance AI with CData Connect AI through the built-in MCP (Model Context Protocol) Server, your agents can query, summarize, and act on live Elasticsearch data in real time. This connection bridges Relevance AI intelligent workflow engine with the governed enterprise connectivity of CData Connect AI ensuring every query runs securely against authorized sources without manual data export.
This article outlines the steps to configure Elasticsearch connectivity in Connect AI, register the CData MCP Server in Relevance AI, and build an agent that interacts with live Elasticsearch data.
About Elasticsearch Data Integration
Accessing and integrating live data from Elasticsearch has never been easier with CData. Customers rely on CData connectivity to:
- Access both the SQL endpoints and REST endpoints, optimizing connectivity and offering more options when it comes to reading and writing Elasticsearch data.
- Connect to virtually every Elasticsearch instance starting with v2.2 and Open Source Elasticsearch subscriptions.
- Always receive a relevance score for the query results without explicitly requiring the SCORE() function, simplifying access from 3rd party tools and easily seeing how the query results rank in text relevance.
- Search through multiple indices, relying on Elasticsearch to manage and process the query and results instead of the client machine.
Users frequently integrate Elasticsearch data with analytics tools such as Crystal Reports, Power BI, and Excel, and leverage our tools to enable a single, federated access layer to all of their data sources, including Elasticsearch.
For more information on CData's Elasticsearch solutions, check out our Knowledge Base article: CData Elasticsearch Driver Features & Differentiators.
Getting Started
Step 1: Configure Elasticsearch Connectivity for Relevance AI
Connectivity to Elasticsearch from Relevance AI is made possible through CData Connect AI's Remote MCP Server. To interact with Elasticsearch data from Relevance AI, we start by creating and configuring a Elasticsearch connection in CData Connect AI.
- Log into Connect AI, click Sources, and then click Add Connection
- Select Elasticsearch from the Add Connection panel
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Enter the necessary authentication properties to connect to Elasticsearch.
Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.
The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.
Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.
- Click Save & Test
- Navigate to the Permissions tab and update user-based permissions
Add a Personal Access Token
A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Relevance AI. It is best practice to create a separate PAT for each integration to maintain granular access control.
- Click the gear icon () at the top right of the Connect AI app to open Settings
- On the Settings page, go to the Access Tokens section and click Create PAT
- Give the PAT a descriptive name and click Create
- Copy the token when displayed and store it securely. It will not be shown again
With the Elasticsearch connection configured and a PAT generated, Relevance AI can now connect to Elasticsearch data through the CData MCP Server.
Step 2: Configure Connectivity in Relevance AI
The CData Connect AI MCP endpoint and authorization details are registered within Relevance AI so that agents can call live data from Connect AI.
- Sign in to Relevance AI and create an account if you do not already have one
- From the sidebar, navigate to Agents and then click on New Agent
- Select Build from scratch and name the agent (eg; CData MCP Server)
- Inside the agent editor, select Advanced and then switch to the MCP Server tab
- Click + Add Remote MCP Tools
- In the dialog that appears, fill out the fields as follows:
- URL: https://mcp.cloud.cdata.com/mcp
- Label: Any custom label (eg; cdata_mcp_server)
- Authentication: Select Custom headers
- Add header key:value pair. Combine your email and PAT as email:PAT and encode that string in Base64 and then prefix with the word Basic
- Key: Authorization
- Value: Basic base64(email:PAT)
Click Connect to establish the connection. Relevance AI will verify your credentials and register the CData Connect AI MCP Server for use in agents.
Step 3: Build and Run a Relevance AI Agent with Live Elasticsearch Data
- Switch to the Run tab for your agent
- Enter a task for example, "List the five most recent incidents from ServiceNow"
- The agent will query Connect AI via the MCP endpoint and display live results from Elasticsearch data
With the connection complete, Relevance AI agents can now issue queries, retrieve records, and perform AI-driven tasks over live Elasticsearch data through CData Connect AI MCP Server.
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