Use Agno to Talk to Your HCL Domino 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 HCL Domino 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 HCL Domino data.

Prerequisites

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

Step 1: Configure HCL Domino in CData Connect AI

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

   Connecting to Domino

   To connect to Domino data, set the following properties:

   • URL: The host name or IP of the server hosting the Domino database. Include the port of the server hosting the Domino database. For example: http://sampleserver:1234/
   • DatabaseScope: The name of a scope in the Domino Web UI. The driver exposes forms and views for the schema governed by the specified scope. In the Domino Admin UI, select the Scopes menu in the sidebar. Set this property to the name of an existing scope.

   Authenticating with Domino

   Domino supports authenticating via login credentials or an Entra ID (formerly Azure AD) OAuth application:

   Login Credentials

   To authenticate with login credentials, set the following properties:

   • AuthScheme: Set this to "OAuthPassword"
   • User: The username of the authenticating Domino user
   • Password: The password associated with the authenticating Domino user

   The driver uses the login credentials to automatically perform an OAuth token exchange.

   EntraID (formerly AzureAD)

   This authentication method uses Entra ID (formerly Azure AD) as an IdP to obtain a JWT token. You need to create a custom OAuth application in Entra ID (formerly Azure AD) and configure it as an IdP. To do so, follow the instructions in the Help documentation. Then set the following properties:

   • AuthScheme: Set this to "EntraID (formerly AzureAD)"
   • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
   • OAuthClientId: The Client ID obtained when setting up the custom OAuth application.
   • OAuthClientSecret: The Client secret obtained when setting up the custom OAuth application.
   • CallbackURL: The redirect URI defined when you registered your app. For example: https://localhost:33333
   • AzureTenant: The Microsoft Online tenant being used to access data. Supply either a value in the form companyname.microsoft.com or the tenant ID.

     The tenant ID is the same as the directory ID shown in the Azure Portal's Entra ID (formerly Azure AD) > Properties page.

   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 HCL Domino 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 HCL Domino data data.

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

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