How to Use the Vectorizer AI MCP in Google ADK

Q: How does Vectorizer AI integrate with Google ADK?

The agent calls the MCP Server directly using vectorizeimage. This allows your Gemini model to reason about image data, decide when vectorization is needed, and execute the conversion step without leaving the Google Cloud environment.

Q: If I need to remove old files, how do I manage that with Vectorizer AI and Google ADK?

You use the deleteimage tool. It’s governed by policy retention days, so it's a reliable mechanism for cleaning up historical JPG or PNG data tokens after they've been processed.

Vectorizing assets for Google ADK enterprise workflows.

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Connect Vectorizer AI MCP to Google ADK

Create your Vinkius account to connect Vectorizer AI 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 Vectorizer AI with Google ADK

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Scale vector conversion with the MCP Server

The `vectorize_image` tool takes bitmaps (JPG, PNG) and converts them to vectors like SVG, EPS, PDF, or DXF. It’s built for high-volume enterprise use cases where you need reliable scaling of design assets. Since you're working on Google Cloud, this ability lets your agent process design input—turning simple raster images into complex vector blueprints—without needing external services.

Managing and downloading data for google-adk

Your agent can pull finished assets using `download_image`. This tool isn't just for fetching results; it lets you grab the output in various formats needed for BigQuery or Vertex AI ingestion. It completes the cycle from input to usable, formatted file. Furthermore, if an asset needs to be removed, `delete_image` handles that cleanup based on policy retention settings.

Monitoring resources via MCP Server

The `get_account_status` tool provides a quick check of the service's subscription status and remaining credits. For long-context reasoning in Google ADK, knowing your resource limits upfront prevents job failures. It gives you the necessary metadata to build robust, self-correcting agents that manage their own operational budget.

Setup guide

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

The agent calls the MCP Server directly using `vectorize_image`. This allows your Gemini model to reason about image data, decide when vectorization is needed, and execute the conversion step without leaving the Google Cloud environment.

Yes. While the input must be JPG or PNG bitmaps, you can request several output formats like SVG, EPS, PDF, and DXF in a single call via `vectorize_image`. This flexibility is key for complex enterprise pipelines.

Always use the account status tool. Before running any major batch of vectorizations, your agent should call `get_account_status` to ensure there are sufficient credits available for the job.

You use the `delete_image` tool. It’s governed by policy retention days, so it's a reliable mechanism for cleaning up historical JPG or PNG data tokens after they've been processed.

The server primarily interacts with the JPG and PNG bitmap image tokens. These are the raw input types handled by `vectorize_image`, making it clear what specific data is being processed by your agent.

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