How to Connect to Live Microsoft Project Data from Claude Agent SDK (via CData Connect AI)

Jerod Johnson
Senior Technology Evangelist

Leverage CData Connect AI to enable Claude Agent SDK applications to securely read and take actions on your Microsoft Project data for you.

Claude Agent SDK is a powerful framework from Anthropic for building production-ready AI agents with stateful conversations, automatic context management, and native MCP (Model Context Protocol) tool integration. When combined with CData Connect AI, you can leverage Claude Agent SDK to build intelligent agents that interact with your Microsoft Project data in real-time through natural language queries. This article outlines the process of connecting to Microsoft Project using Connect AI and configuring a Claude Agent SDK application to interact with your Microsoft Project data.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Microsoft Project data. The CData Connect AI Remote MCP Server enables secure communication between Claude Agent SDK applications and Microsoft Project. This allows your agents to read from and take actions on your Microsoft Project data, all without the need for data replication to a natively supported database. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Microsoft Project. This leverages server-side processing to swiftly deliver the requested Microsoft Project data.

In this article, we show how to configure a Claude Agent SDK application to conversationally explore (or Vibe Query) your data using natural language. With Connect AI you can build agents with access to live Microsoft Project data, plus hundreds of other sources.

Step 1: Configure Microsoft Project Connectivity for Claude Agent SDK

Connectivity to Microsoft Project from Claude Agent SDK applications is made possible through CData Connect AI Remote MCP. To interact with Microsoft Project data from your agent, we start by creating and configuring a Microsoft Project connection in CData Connect AI.

Log into Connect AI, click Sources, and then click Add Connection

Select "Microsoft Project" from the Add Connection panel

Enter the necessary authentication properties to connect to Microsoft Project.
The User and Password properties, under the Authentication section, must be set to valid Microsoft Project user credentials. In addition, specify a URL to a valid Microsoft Project server organization root or Microsoft Project services file.
Click Save & Test
Navigate to the Permissions tab in the Add Microsoft Project Connection page and update the User-based permissions.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from your Claude Agent SDK application. It is best practice to create a separate PAT for each service to maintain granularity of access.

Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
On the Settings page, go to the Access Tokens section and click Create PAT.
Give the PAT a name and click Create.
The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, we are ready to connect to Microsoft Project data from your Claude Agent SDK application.

Step 2: Set Up Your Claude Agent SDK Application for CData Connect AI

Follow these steps to configure your Claude Agent SDK application to connect to CData Connect AI. You can use our pre-built agent chatbot as a starting point, available at https://github.com/CDataSoftware/connect-ai-claude-agent, or follow the instructions below to create your own.

Prerequisites

Python 3.8 or higher installed on your system
An Anthropic API key (obtain from https://console.anthropic.com/)

Install the required dependencies:

pip install claude-agent-sdk python-dotenv requests

Configuration Steps

Create a new Python project directory and navigate to it:
```
mkdir my-cdata-agent
cd my-cdata-agent
```
Set up your environment variables. Create a .env file in your project root with the following variables:
```
ANTHROPIC_API_KEY=your_anthropic_api_key
[email protected]
CDATA_ACCESS_TOKEN=your_personal_access_token
```
Replace your_anthropic_api_key with your Anthropic API key, [email protected] with your Connect AI email address, and your_personal_access_token with the Personal Access Token created in Step 1.

Building Your Claude Agent SDK Application

With the setup for CData Connect AI completed, we are ready to build our agent application. We will walk through the key components needed to create a production-ready AI agent with stateful conversations and native MCP tool integration. For this example, we will create a single Python file (agent.py), which includes the MCP client, agent chatbot, and interactive interface.

Imports and Environment Setup

To start, import the necessary libraries and load environment variables. We need the Claude Agent SDK for agent functionality, the requests library for HTTP communication with the MCP server, and standard Python libraries for async operations and authentication.


#!/usr/bin/env python3
import os
import json
import base64
import asyncio
from typing import Optional, Dict, Any
from functools import partial
import requests
from claude_agent_sdk import query, ClaudeSDKClient, ClaudeAgentOptions, tool, create_sdk_mcp_server
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

Creating the MCP Client

The MCPClient class handles communication with the CData Connect AI MCP server over HTTP. It manages authentication using Basic Auth (email and personal access token), sends JSON-RPC requests to the MCP server, and parses Server-Sent Events (SSE) responses. The client provides two key methods: list_tools() to discover available tools and call_tool() to execute MCP tools.


class MCPClient:
    """Client for interacting with CData Connect AI MCP server over HTTP."""

    def __init__(self, server_url: str, email: Optional[str] = None, access_token: Optional[str] = None):
        self.server_url = server_url.rstrip('/')
        self.session = requests.Session()

        # Set default headers for MCP JSON-RPC
        self.session.headers.update({
            'Content-Type': 'application/json',
            'Accept': 'application/json, text/event-stream'
        })

        if email and access_token:
            # Basic authentication: email:personal_access_token
            credentials = f"{email}:{access_token}"
            encoded_credentials = base64.b64encode(credentials.encode()).decode()
            self.session.headers.update({
                'Authorization': f'Basic {encoded_credentials}'
            })

    def _parse_sse_response(self, response_text: str) -> dict:
        """Parse Server-Sent Events (SSE) response."""
        for line in response_text.split('\n'):
            if line.startswith('data: '):
                data_json = line[6:]  # Remove 'data: ' prefix
                return json.loads(data_json)
        raise ValueError("No data found in SSE response")

    def list_tools(self) -> list:
        """Get available tools from the MCP server."""
        response = self.session.post(
            self.server_url,
            json={
                "jsonrpc": "2.0",
                "method": "tools/list",
                "params": {},
                "id": 1
            }
        )
        response.raise_for_status()
        result = self._parse_sse_response(response.text)
        return result.get("result", {}).get("tools", [])

    def call_tool(self, tool_name: str, arguments: dict) -> dict:
        """Call a tool on the MCP server."""
        response = self.session.post(
            self.server_url,
            json={
                "jsonrpc": "2.0",
                "method": "tools/call",
                "params": {
                    "name": tool_name,
                    "arguments": arguments
                },
                "id": 2
            }
        )
        response.raise_for_status()
        result = self._parse_sse_response(response.text)
        return result.get("result", {})

Building the Agent Chatbot Class

The MCPAgentChatbot class is the core of our application. It connects the MCP client to the Claude Agent SDK, creating a production-ready AI agent with stateful conversations and automatic context management. The class performs several key functions during initialization: it creates an MCP client instance, discovers available tools from the CData Connect AI MCP server, wraps each MCP tool for use with the Agent SDK, and creates an SDK-compatible MCP server.

Initializing the Agent and Loading Tools

When the MCPAgentChatbot is initialized, it connects to the CData Connect AI MCP server and retrieves all available tools. These tools are then wrapped to be compatible with the Claude Agent SDK's tool framework.


class MCPAgentChatbot:
    """
    Production-ready AI agent chatbot for CData Connect AI.
    Uses Claude Agent SDK for stateful conversations and automatic context management.
    """

    def __init__(self, mcp_server_url: str, email: Optional[str] = None, access_token: Optional[str] = None):
        self.mcp_client = MCPClient(mcp_server_url, email, access_token)

        # Load available tools from MCP server
        print("Connecting to CData Connect AI MCP server...")
        self.mcp_tools_list = self.mcp_client.list_tools()
        print(f"✓ Loaded {len(self.mcp_tools_list)} tools from MCP server")

        # Create custom MCP tool wrappers for the Agent SDK
        self.agent_tools = self._create_agent_tools()

        # Create MCP server for Agent SDK
        self.mcp_server = create_sdk_mcp_server(
            name="cdata_connect",
            tools=self.agent_tools
        )

Creating Tool Wrappers for the Agent SDK

The _create_agent_tools() method transforms MCP tool definitions into Agent SDK-compatible tools. For each tool discovered from the MCP server, it creates a wrapper using the @tool decorator with the tool's name, description, and input schema. The _tool_handler() method is called whenever a tool is executed - it forwards the request to the MCP server and formats the response for the Agent SDK.


    async def _tool_handler(self, tool_name: str, args: Dict[str, Any]) -> Dict[str, Any]:
        """Call the MCP tool and return results."""
        result = self.mcp_client.call_tool(tool_name, args)
        return {
            "content": [{
                "type": "text",
                "text": json.dumps(result, indent=2)
            }]
        }

    def _create_agent_tools(self) -> list:
        """Create Agent SDK tool wrappers for MCP tools."""
        agent_tools = []
        for tool_info in self.mcp_tools_list:
            tool_name = tool_info.get("name")
            tool_description = tool_info.get("description", "")
            tool_schema = tool_info.get("inputSchema", {})

            agent_tool = tool(
                name=tool_name,
                description=tool_description,
                input_schema=tool_schema
            )(partial(self._tool_handler, tool_name))

            agent_tools.append(agent_tool)
        return agent_tools

Managing Stateful Conversation Sessions

One of the key features of the Claude Agent SDK is its ability to maintain stateful conversations with automatic context management. The create_session() method creates a ClaudeSDKClient instance configured with a system prompt, access to the MCP tools, and permission settings. The chat_session() method sends a user message and retrieves the agent's response, maintaining conversation context automatically.


    def create_session(self) -> ClaudeSDKClient:
        """Create a stateful conversation session."""
        options = ClaudeAgentOptions(
            system_prompt="You are a helpful assistant with access to Microsoft Project data through CData Connect AI.",
            mcp_servers={"cdata_connect": self.mcp_server},
            permission_mode="bypassPermissions"
        )
        return ClaudeSDKClient(options=options)

    async def chat_session(self, client: ClaudeSDKClient, user_message: str) -> str:
        """Send a message in a stateful session."""
        await client.query(user_message)
        async for message in client.receive_response():
            if hasattr(message, 'result'):
                return str(message.result)
        return ""

Creating the Interactive Interface

The main() function brings everything together to create an interactive chatbot. It loads credentials from environment variables, initializes the MCPAgentChatbot, creates a stateful session, and starts an interactive loop where users can chat with the agent. The session is managed using Python's async context manager (async with), which ensures proper cleanup of resources and API connections.


async def main():
    """Run the agent in interactive mode."""
    MCP_SERVER_URL = "https://mcp.cloud.cdata.com/mcp/"
    CDATA_EMAIL = os.environ.get("CDATA_EMAIL")
    CDATA_ACCESS_TOKEN = os.environ.get("CDATA_ACCESS_TOKEN")

    print("=== CData Connect AI Agent ===\n")

    chatbot = MCPAgentChatbot(MCP_SERVER_URL, CDATA_EMAIL, CDATA_ACCESS_TOKEN)
    client = chatbot.create_session()

    async with client:
        while True:
            user_input = input("You: ").strip()
            if user_input.lower() in ['quit', 'exit', 'q']:
                break
            response = await chatbot.chat_session(client, user_input)
            print(f"\nAssistant: {response}\n")


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

Running Your Agent

Save all the code sections above into a single file named agent.py in your project directory. Alternatively, you can download the complete example from https://github.com/CDataSoftware/connect-ai-claude-agent.
Run your agent application:
```
python agent.py
```
Start interacting with your Microsoft Project data through natural language queries. Your agent now has access to your Microsoft Project data through the CData Connect AI MCP Server.

Step 3: Build Intelligent Agents with Live Microsoft Project Data Access

With your Claude Agent SDK application configured and connected to CData Connect AI, you can now build sophisticated agents that interact with your Microsoft Project data using natural language. The MCP integration provides your agents with powerful data access capabilities.

Available MCP Tools for Your Agent

Your Claude Agent SDK application has access to the following CData Connect AI MCP tools:

queryData: Execute SQL queries against connected data sources and retrieve results
getCatalogs: Retrieve a list of available connections from CData Connect AI
getSchemas: Retrieve database schemas for a specific catalog
getTables: Retrieve database tables for a specific catalog and schema
getColumns: Retrieve column metadata for a specific table
getProcedures: Retrieve stored procedures for a specific catalog and schema
getProcedureParameters: Retrieve parameter metadata for stored procedures
executeProcedure: Execute stored procedures with parameters

Key Features of Claude Agent SDK

The Claude Agent SDK provides several production-ready features that make it ideal for building AI agents:

Automatic Context Management: No manual conversation history tracking needed - the SDK handles context automatically
Stateful Conversations: Maintain context across multiple turns for natural, flowing interactions
Native Tool Integration: Tools are first-class citizens with automatic calling lifecycle and error handling
Context Compaction: Automatic context compaction when approaching token limits
Async/Await Architecture: Modern Python async patterns for better performance
Permission Controls: Fine-grained control over tool permissions and execution

Example Use Cases

Here are some examples of what your Claude Agent SDK applications can do with live Microsoft Project data access:

Data Analysis Agent: Build an agent that analyzes trends, patterns, and anomalies in your Microsoft Project data
Report Generation Agent: Create agents that generate custom reports based on natural language requests
Data Quality Agent: Develop agents that monitor and validate data quality in real-time
Business Intelligence Agent: Build agents that answer complex business questions by querying multiple data sources
Interactive Chatbot: Create chatbots that can answer questions about your Microsoft Project data in natural language
Automated Workflow Agent: Create agents that trigger actions based on data conditions in Microsoft Project

Testing Your Agent

Once your agent is running, you can interact with it through natural language queries. For example:

"Show me all customers from the last 30 days"
"What are the top performing products this quarter?"
"Analyze sales trends and identify anomalies"
"Generate a summary report of active projects"
"Find all records that match specific criteria"

Your Claude Agent SDK application will automatically translate these natural language queries into appropriate SQL queries and execute them against your Microsoft Project data through the CData Connect AI MCP Server, providing real-time insights without requiring users to write complex SQL or understand the underlying data structure.

Advanced Configuration

Programmatic Usage

You can also use the Claude Agent SDK programmatically for one-off queries or integration into larger applications:

One-off Queries

from claude_agent_sdk import query, ClaudeAgentOptions

async def single_query():
    options = ClaudeAgentOptions(
        system_prompt="You are a data analyst assistant.",
        mcp_servers={"cdata_connect": mcp_server},
        permission_mode="bypassPermissions"
    )

    response = await query(
        prompt="What data sources are available?",
        options=options
    )

    async for message in response:
        if hasattr(message, 'result'):
            print(message.result)

Stateful Multi-Turn Conversations

async def conversation():
    client = chatbot.create_session()

    async with client:
        # First query
        response1 = await chatbot.chat_session(client, "List my data sources")
        print(response1)

        # Follow-up query with context
        response2 = await chatbot.chat_session(client, "Tell me more about the first one")
        print(response2)

Permission Modes

Claude Agent SDK offers different permission modes for tool execution:

default: Prompts user for approval before executing tools
acceptEdits: Automatically accepts edit operations
bypassPermissions: Auto-approves all tools (recommended for CLI applications)
plan: Agent plans actions before execution

Get CData Connect AI

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