How to Use the AiMasher MCP in Google ADK

Q: Can I filter tools when using AiMasher with Google ADK?

You can use the toolnames filter to restrict the server to specific functions. This limits your agent's footprint and prevents accidental calls to sensitive tools.

Deploy enterprise agents using AiMasher and Google ADK to link your marketing campaigns directly to your BigQuery data.

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Connect AiMasher MCP to Google ADK

Create your Vinkius account to connect AiMasher 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 AiMasher with Google ADK

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Enterprise campaigns on Google ADK

Manage your marketing pipeline by calling `list_campaigns` through your Google ADK agent. This lets you pull active data into your Gemini-powered workflows. Since this runs on Google Cloud, your agent gains direct access to your existing infrastructure. You can feed campaign details into your broader data analysis scripts without moving files manually.

Retrieve templates with Google ADK

Use `get_template` to fetch specific layouts for your assets. The agent processes these templates against your long-context window. This approach allows you to iterate on creative assets while maintaining context across a million tokens. Your agent stays grounded in the specific template structure provided by the server.

Track campaign outputs

Call `list_outputs` to review generated content within your Google ADK environment. You keep a clear audit trail of what the AI produced for each campaign. This visibility helps when you need to reconcile creative output against your core business metrics. You get the raw data directly in your agent's reasoning loop.

Setup guide

Set up AiMasher 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 AiMasher 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="AiMasher_agent",
    model="gemini-2.0-flash",
    instruction="You have access to AiMasher 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 AiMasher. 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 AiMasher MCP in Google ADK

You initialize a McpToolset using your server URL. Inject this toolset into your LlmAgent to give your code access to the full suite of marketing operations.

It acts as a standard tool provider. Because the ADK supports both Stdio and HTTP, you can place this server anywhere inside your VPC to keep your data close to your BigQuery instances.

You can use the tool_names filter to restrict the server to specific functions. This limits your agent's footprint and prevents accidental calls to sensitive tools.

The Gemini model consumes the raw JSON output from the server. It reasons over this information to decide the next step in your enterprise workflow.

Your marketing campaign metadata is scoped to your specific endpoint token. Every request is proxied through the Vinkius sandbox, stripping headers and ensuring the connection remains ephemeral.

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