How to Use the Adobe Analytics MCP in Google ADK

Q: Can Google ADK filter which Adobe Analytics tools are exposed?

You can restrict access using the toolnames parameter. This prevents a basic reporting agent from accidentally running segment modifications.

Feed your Google ADK enterprise agents direct customer journey data from Adobe Analytics.

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Connect Adobe Analytics MCP to Google ADK

Create your Vinkius account to connect Adobe Analytics to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Adobe Analytics with Google ADK

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Process massive reports with Gemini

Enterprise web traffic generates massive payloads. Calling `get_report` returns raw JSON that easily chokes smaller models, but Gemini's 1M+ token context window swallows it whole. Your agent reads the entire historical dataset in one pass. It cross-references those numbers against internal knowledge bases without needing a vector database middleman.

Connect Adobe Analytics to your Google ADK MCP Server

Bridging your existing BigQuery data with live web metrics is now trivial. The agent runs `list_report_suites` to find the right property, then pulls fresh numbers to compare against your warehouse. Mapping the schema happens automatically. Executing `get_dimensions` and `get_metrics` gives the agent the exact variable names needed to write accurate SQL for Vertex AI tasks.

Target audience segments

Marketing teams need to know who is visiting the site. Invoking `list_segments` pulls the active audience definitions straight from the source. You pass the MCP Toolset directly to your LlmAgent constructor. The system handles the HTTP transport routing behind the scenes.

Setup guide

Set up Adobe Analytics MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Adobe Analytics tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Adobe Analytics_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Adobe Analytics tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Adobe Analytics. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Real-time monitoring

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Common questions about Adobe Analytics MCP in Google ADK

Initialize an MCP Toolset using the Vinkius MCP Server URL. Pass that object to your LlmAgent tools array. The framework handles the rest.

You can restrict access using the tool_names parameter. This prevents a basic reporting agent from accidentally running segment modifications.

It works perfectly alongside Vertex AI tools. Your agent can pull web traffic via this MCP Server and immediately feed it into a custom Vertex pipeline.

The framework supports both Stdio and HTTP transports. Vinkius relies on the Streamable HTTP parameters to maintain the connection.

Zero-trust architecture means no standing access. Audience segments and visitor metrics flow through an isolated instance that requires a single endpoint token, never storing your proprietary marketing data at rest.

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