Use Agno to Talk to Your Excel Online 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 Excel Online 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 Excel Online data.

Prerequisites

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

Step 1: Configure Excel Online in CData Connect AI

To enable Agno to query live Excel Online data, first create a Excel Online 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 "Excel Online" from the Add Connection panel.
3. Enter the required authentication properties.

   You can connect to a workbook by providing authentication to Excel Online and then setting the following properties:

   • Workbook: Set this to the name or Id of the workbook.

     If you want to view a list of information about the available workbooks, execute a query to the Workbooks view after you authenticate.

   • UseSandbox: Set this to true if you are connecting to a workbook in a sandbox account. Otherwise, leave this blank to connect to a production account.

   You use the OAuth authentication standard to authenticate to Excel Online. See the Getting Started section in the help documentation for a guide. Getting Started also guides you through executing SQL to worksheets and ranges.

   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 Excel Online 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 Excel Online data data.

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

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