How to Use the Cakemail MCP in Google ADK

Q: Can I filter which tools my Google ADK agent sees for Cakemail?

You can use the toolnames filter to restrict access. This ensures your agent only uses the specific functions you explicitly allow.

Q: Will my Google ADK agent work with large Cakemail datasets?

Yes, Gemini models with 1M+ tokens handle the output from listcontacts easily. This allows for deep reasoning over large mailing lists.

Connect Cakemail to your Google ADK agent for enterprise-grade marketing automation on Google Cloud.

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Connect Cakemail MCP to Google ADK

Create your Vinkius account to connect Cakemail 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 Cakemail with Google ADK

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Execute Cakemail tasks in Google ADK

Your agent uses `create_campaign` to launch outreach efforts directly from your Gemini-powered pipeline. It fits right into your existing Google Cloud infrastructure. By calling `get_campaign`, the agent fetches specific details to inform its next reasoning step. This tight loop keeps your marketing operations fast.

Sync contact lists with Google ADK

The agent interacts with your lists by using `create_contact_list` to segment users based on your BigQuery insights. It automates the data flow between your cloud warehouse and your email provider. Running `list_contact_lists` lets the agent verify where it should add new leads. It eliminates the need for manual list management.

Access marketing analytics via MCP Server

Your agent pulls metrics using `get_analytics` to analyze performance across your campaigns. It provides the feedback loop needed for long-context reasoning with Gemini models. Calling `list_contacts` gives your agent the context required for high-volume outreach. It keeps your operations precise and data-driven.

Setup guide

Set up Cakemail 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 Cakemail 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="Cakemail_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Cakemail 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 Cakemail. 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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Common questions about Cakemail MCP in Google ADK

You use the McpToolset class and pass your server parameters into your LlmAgent. It supports both Stdio and HTTP transports depending on your deployment.

You can use the tool_names filter to restrict access. This ensures your agent only uses the specific functions you explicitly allow.

Your credentials stay within the Vinkius managed environment. The agent only gets access to the tools it needs to execute your specific tasks.

Yes, Gemini models with 1M+ tokens handle the output from `list_contacts` easily. This allows for deep reasoning over large mailing lists.

The server operates in a V8-isolated sandbox that wipes memory after every request. Your contact list data never persists outside of your active session.

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