Use Agno to Talk to Your Elasticsearch Data via CData Connect AI

Agno is a developer-first Python framework for building AI agents that reason, plan, and take actions using tools. Agno emphasizes a clean, code-driven architecture where the agent runtime remains fully under developer control.

CData Connect AI provides a secure cloud-to-cloud interface for integrating hundreds of enterprise data sources with AI systems. Using Connect AI, live Elasticsearch data data can be exposed through a remote MCP endpoint without replication.

In this guide, we build a production-ready Agno agent using the Agno Python SDK. The agent connects to CData Connect AI via MCP using streamable HTTP, dynamically discovers available tools, and invokes them to query live Elasticsearch data.

Prerequisites

1. Python 3.9+.
2. A CData Connect AI account – Sign up or log in here.
3. An active Elasticsearch account with valid credentials.
4. An LLM API key (for example, OpenAI).

Overview

Here is a high-level overview of the process:

1. Connect: Configure a Elasticsearch connection in CData Connect AI.
2. Discover: Use MCP to dynamically retrieve tools exposed by CData Connect AI.
3. Query: Wrap MCP tools as Agno functions and query live Elasticsearch data.

Getting Started

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Step 1: Configure Elasticsearch in CData Connect AI

To enable Agno to query live Elasticsearch data, first create a Elasticsearch connection in CData Connect AI. This connection is exposed through the CData Remote MCP Server.

1. Log into Connect AI, click Sources, and then click Add Connection. 👁 Adding a connection
   
2. Select "Elasticsearch" from the Add Connection panel. 👁 Selecting a data source
   
3. Enter the required authentication properties.

   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.

   👁 Configuring connection properties
   Click Create & Test.
4. Open the Permissions tab and configure user access. 👁 Updating permissions
   

Add a Personal Access Token

A Personal Access Token (PAT) authenticates MCP requests from Agno to CData Connect AI.

1. Open Settings and navigate to Access Tokens.
2. Click Create PAT.
3. Save the generated token securely. 👁 Creating a PAT
   

Step 2: Install dependencies and configure environment variables

Install Agno and the MCP adapter dependencies. LangChain is included strictly for MCP tool compatibility.

pip install agno agno-mcp langchain-mcp-adapters

Configure environment variables:

export CDATA_MCP_URL="https://mcp.cloud.cdata.com/mcp"
export CDATA_MCP_AUTH="Base64EncodedCredentials"
export OPENAI_API_KEY="your-openai-key"

Where "Base64EncodedCredentials" is your Connect AI user email and your Personal Access Token joined by a colon (":") and Base64 Encoded: Base64([email protected]:MY_CONNECT_AI_PAT)

Step 3: Connect to CData Connect AI via MCP

Create an MCP client using streamable HTTP. This establishes a secure connection to CData Connect AI.

import os
from langchain_mcp_adapters.client import MultiServerMCPClient

mcp_client = MultiServerMCPClient(
 connections={
 "default": {
 "transport": "streamable_http",
 "url": os.environ["CDATA_MCP_URL"],
 "headers": {
 "Authorization": f"Basic {os.environ['CDATA_MCP_AUTH']}"
 }
 }
 }
)

Step 4: Discover MCP tools

CData Connect AI exposes operations as MCP tools. These are retrieved dynamically at runtime.

langchain_tools = await mcp_client.get_tools()
for tool in langchain_tools:
 print(tool.name)

Step 5: Convert MCP tools to Agno functions

Each MCP tool is wrapped as an Agno function so it can be used by the agent.

NOTE: Agno performs all reasoning, planning, and tool selection.LangChain is used only as a lightweight MCP compatibility layer to consume tools exposed by CData Connect AI.

from agno.tools import Function

def make_tool_caller(lc_tool):
 async def call_tool(**kwargs):
 return await lc_tool.ainvoke(kwargs)
 return call_tool

Step 6: Create an Agno agent and query live Elasticsearch data

Agno performs all reasoning, planning, and tool invocation. LangChain plays no role beyond MCP compatibility.

from agno.agent import Agent
from agno.models.openai import OpenAIChat

agent = Agent(
 model=OpenAIChat(
 id="gpt-4o",
 temperature=0.2,
 api_key=os.environ["OPENAI_API_KEY"]
 ),
 tools=agno_tools,
 markdown=True
)

await agent.aprint_response(
 "Show me the top 5 records from the available data source"
)

if __name__ == "__main__":
 asyncio.run(main())

The results below show an Agno agent invoking MCP tools through CData Connect AI and returning live Elasticsearch data data.

👁 Running the Agno agent
👁 Agno agent output

You can now query live Elasticsearch data using natural language through your Agno agent.

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