Use Agno to Talk to Your SQL Server 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 300+ enterprise data sources with AI systems. Using Connect AI, live SQL Server 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 SQL Server data.

Prerequisites

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

Step 1: Configure SQL Server in CData Connect AI

To enable Agno to query live SQL Server data, first create a SQL Server 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.
2. Select "SQL Server" from the Add Connection panel.
3. Enter the required authentication properties.

   Connecting to Microsoft SQL Server

   Connect to Microsoft SQL Server using the following properties:

   • Server: The name of the server running SQL Server.
   • User: The username provided for authentication with SQL Server.
   • Password: The password associated with the authenticating user.
   • Database: The name of the SQL Server database.

   Connecting to Azure SQL Server and Azure Data Warehouse

   You can authenticate to Azure SQL Server or Azure Data Warehouse by setting the following connection properties:

   • Server: The server running Azure. You can find this by logging into the Azure portal and navigating to "SQL databases" (or "SQL data warehouses") -> "Select your database" -> "Overview" -> "Server name."
   • User: The name of the user authenticating to Azure.
   • Password: The password associated with the authenticating user.
   • Database: The name of the database, as seen in the Azure portal on the SQL databases (or SQL warehouses) page.

   SSH Connectivity for SQL Server

   You can use SSH (Secure Shell) to authenticate with SQL Server, whether the instance is hosted on-premises or in supported cloud environments. SSH authentication ensures that access is encrypted (as compared to direct network connections).

   SSH Connections to SQL Server in Password Auth Mode

   To connect to SQL Server via SSH in Password Auth mode, set the following connection properties:

   • User: SQL Server User name
   • Password: SQL Server Password
   • Database: SQL Server database name
   • Server: SQL Server Server name
   • Port: SQL Server port number like 3306
   • UserSSH: "true"
   • SSHAuthMode: "Password"
   • SSHPort: SSH Port number
   • SSHServer: SSH Server name
   • SSHUser: SSH User name
   • SSHPassword: SSH Password

   SSH Connections to SQL Server in Public Key Auth Mode

   To connect to SQL Server via SSH in Password Auth mode, set the following connection properties:

   • User: SQL Server User name
   • Password: SQL Server Password
   • Database: SQL Server database name
   • Server: SQL Server Server name
   • Port: SQL Server port number like 3306
   • UserSSH: "true"
   • SSHAuthMode: "Public_Key"
   • SSHPort: SSH Port number
   • SSHServer: SSH Server name
   • SSHUser: SSH User name
   • SSHClientCret: the path for the public key certificate file
   Click Create & Test.
4. Open the Permissions tab and configure user access.

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.

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 SQL Server 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 SQL Server data data.

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

Get CData Connect AI

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