Use Agno to Talk to Your SingleStore 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 SingleStore 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 SingleStore data.

Prerequisites

1. Python 3.9+.
2. A CData Connect AI account – Sign up or log in here.
3. An active SingleStore 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 SingleStore 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 SingleStore data.

Step 1: Configure SingleStore in CData Connect AI

To enable Agno to query live SingleStore data, first create a SingleStore 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 "SingleStore" from the Add Connection panel. 👁 Selecting a data source
   
3. Enter the required authentication properties.

   The following connection properties are required in order to connect to data.

   • Server: The host name or IP of the server hosting the SingleStore database.
   • Port: The port of the server hosting the SingleStore database.
   • Database (Optional): The default database to connect to when connecting to the SingleStore Server. If this is not set, tables from all databases will be returned.

   Connect Using Standard Authentication

   To authenticate using standard authentication, set the following:

   • User: The user which will be used to authenticate with the SingleStore server.
   • Password: The password which will be used to authenticate with the SingleStore server.

   Connect Using Integrated Security

   As an alternative to providing the standard username and password, you can set IntegratedSecurity to True to authenticate trusted users to the server via Windows Authentication.

   Connect Using SSL Authentication

   You can leverage SSL authentication to connect to SingleStore data via a secure session. Configure the following connection properties to connect to data:

   • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
   • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
   • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
   • SSLClientCertType: The certificate type of the client store.
   • SSLServerCert: The certificate to be accepted from the server.

   Connect Using SSH Authentication

   Using SSH, you can securely login to a remote machine. To access SingleStore data via SSH, configure the following connection properties:

   • SSHClientCert: Set this to the name of the certificate store for the client certificate.
   • SSHClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
   • SSHClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
   • SSHClientCertType: The certificate type of the client store.
   • SSHPassword: The password that you use to authenticate with the SSH server.
   • SSHPort: The port used for SSH operations.
   • SSHServer: The SSH authentication server you are trying to authenticate against.
   • SSHServerFingerPrint: The SSH Server fingerprint used for verification of the host you are connecting to.
   • SSHUser: Set this to the username that you use to authenticate with the SSH server.
   👁 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 SingleStore 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 SingleStore data data.

👁 Running the Agno agent
👁 Agno agent output

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

---

Get CData Connect AI

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications, try CData Connect AI today!