# Windsor.ai MCP tool for ADK

Supported in ADKPythonTypeScript

The [Windsor MCP Server](https://github.com/windsor-ai/windsor_mcp) connects your ADK agent to [Windsor.ai](https://windsor.ai/), a data integration platform that unifies marketing, sales, and customer data from 325+ sources. This integration gives your agent the ability to query and analyze cross-channel business data using natural language, without writing SQL or custom scripts.

## Use cases

- **Marketing Performance Analysis**: Analyze campaign performance across channels like Facebook Ads, Google Ads, TikTok Ads, and more. Ask questions like "What campaigns had the best ROAS last month?" and get instant insights.
- **Cross-Channel Reporting**: Generate comprehensive reports combining data from multiple platforms such as GA4, Shopify, Salesforce, and HubSpot to get a unified view of business performance.
- **Budget Optimization**: Identify underperforming campaigns, detect budget inefficiencies, and get AI-driven recommendations for spend allocation across advertising channels.

## Prerequisites

- A [Windsor.ai](https://windsor.ai/) account with connected data sources
- A Windsor.ai API key (obtain from [onboard.windsor.ai](https://onboard.windsor.ai))

## Use with agent

```python
import os
from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams

# Required for recursive $ref in MCP schema (https://github.com/google/adk-python/issues/3870)
os.environ["ADK_ENABLE_JSON_SCHEMA_FOR_FUNC_DECL"] = "1"

WINDSOR_API_KEY = "YOUR_WINDSOR_API_KEY"

root_agent = Agent(
    model="gemini-flash-latest",
    name="windsor_agent",
    instruction="Help users analyze their marketing and business data.",
    tools=[
        McpToolset(
            connection_params=StreamableHTTPConnectionParams(
                url="https://mcp.windsor.ai",
                headers={
                    "Authorization": f"Bearer {WINDSOR_API_KEY}",
                },
            ),
        )
    ],
)
```

```typescript
import { LlmAgent, MCPToolset } from "@google/adk";

const WINDSOR_API_KEY = "YOUR_WINDSOR_API_KEY";

const rootAgent = new LlmAgent({
    model: "gemini-flash-latest",
    name: "windsor_agent",
    instruction: "Help users analyze their marketing and business data.",
    tools: [
        new MCPToolset({
            type: "StreamableHTTPConnectionParams",
            url: "https://mcp.windsor.ai",
            transportOptions: {
                requestInit: {
                    headers: {
                        Authorization: `Bearer ${WINDSOR_API_KEY}`,
                    },
                },
            },
        }),
    ],
});

export { rootAgent };
```

## Capabilities

Windsor MCP provides a natural language interface to your integrated business data. Rather than exposing discrete tools, it interprets your questions and returns structured insights from your connected data sources.

| Capability           | Description                                                        |
| -------------------- | ------------------------------------------------------------------ |
| Data querying        | Query normalized data from any of your 325+ connected platforms    |
| Performance analysis | Analyze KPIs, trends, and campaign metrics across channels         |
| Report generation    | Create marketing dashboards and cross-channel performance reports  |
| Budget analysis      | Identify spend inefficiencies and get optimization recommendations |
| Anomaly detection    | Detect outliers and unusual patterns in performance data           |

## Supported data sources

Windsor.ai connects to 325+ platforms, including:

- **Advertising**: Facebook Ads, Google Ads, TikTok Ads, LinkedIn Ads, Microsoft Ads
- **Analytics**: Google Analytics 4, Adobe Analytics
- **CRM**: Salesforce, HubSpot
- **E-commerce**: Shopify
- **And more**: See the [full list of connectors](https://windsor.ai/) on the Windsor.ai website

## Additional resources

- [Windsor MCP Server Repository](https://github.com/windsor-ai/windsor_mcp)
- [Windsor.ai Documentation](https://windsor.ai/documentation/windsor-mcp/)
- [Windsor MCP Introduction](https://windsor.ai/introducing-windsor-mcp/)
- [Windsor MCP Use Cases & Examples](https://windsor.ai/how-to-use-windsor-mcp-examples-use-cases/)