How to Use the MemeGen API MCP in Google ADK

Q: Can Gemini use Google ADK to search for specific templates?

Yes, the agent uses the MCP tool searchmemetemplates to query the database using natural language. Gemini's reasoning engine translates the user's intent into search terms to find the closest match.

Q: Can I restrict which tools are exposed to my Gemini model?

Yes, the SDK allows you to pass a toolnames filter to your toolset config. This lets you expose only createcustommeme while hiding configuration tools like font lists if they aren't needed.

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Connect MemeGen API MCP to Google ADK

Create your Vinkius account to connect MemeGen API 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 MemeGen API with Google ADK

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Search templates using Google ADK

The `search_meme_templates` tool connects Gemini's long-context reasoning with our image library to find relevant templates for your enterprise reports. By exposing this tool through an MCP Server to your Google ADK, your agent can scan thousands of internal documents or BigQuery rows and match them with the perfect visual metaphor. The tool returns precise metadata that Gemini processes natively. This means your agent can intelligently select the right image template without you having to hardcode any mapping logic.

Create images with Google ADK integration

The `create_custom_meme` tool renders the final image URL using the text generated by your Gemini model. When integrated via the Google ADK, your agent can pull metrics directly from your cloud database and write them onto the template in one continuous execution step. This setup avoids the need for external glue code. The tool executes within the framework's standard toolset execution flow, returning a clean URL that your agent can immediately post back to your team or save to cloud storage.

Verify system readiness via MCP Server

The `check_api_status` tool lets your Gemini agent verify that the image rendering service is fully operational before executing long-running batch jobs. By calling this tool, the agent can pause or queue tasks if the endpoint is undergoing maintenance. Your agent can also call `list_meme_fonts` and `list_meme_templates` to cache the available styling options. This caching reduces latency during high-volume generation runs across your Google Cloud infrastructure.

Setup guide

Set up MemeGen API 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 MemeGen API 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="MemeGen API_agent",
    model="gemini-2.0-flash",
    instruction="You have access to MemeGen API tools via MCP.",
    tools=mcp_tools,
)

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Common questions about MemeGen API MCP in Google ADK

Instantiate McpToolset with the MCP Server HTTP URL and pass it into your LlmAgent tools list. The Gemini model will automatically detect the tools and call them when needed.

Yes, the agent uses the MCP tool `search_meme_templates` to query the database using natural language. Gemini's reasoning engine translates the user's intent into search terms to find the closest match.

The server is hosted on Vinkius's scalable infrastructure, allowing your agent to call `create_custom_meme` repeatedly during batch operations. You can also monitor performance using Gemini's native execution logging.

Yes, the SDK allows you to pass a tool_names filter to your toolset config. This lets you expose only `create_custom_meme` while hiding configuration tools like font lists if they aren't needed.

The MCP Server operates on a zero-trust architecture where your template selections and text strings are processed in memory solely to render the image. No data is saved or used for training, keeping your internal cloud metrics completely private.

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