How to Use the CometAPI MCP in Google ADK

Q: Can my Google ADK agent query usage statistics from CometAPI?

Yes, your agent can call getapiusagestatistics to read your current usage metrics. This is especially useful for tracking cross-provider costs alongside your Google Cloud BigQuery bills.

Q: How does Gemini leverage CometAPI to list available models?

The agent invokes listavailableaimodels to inspect the available backend engines. This lets your Google ADK pipeline dynamically decide which model to target for specific translation or generation tasks.

Q: How are my generated images and text prompts protected when using CometAPI?

Every request sent to generateaiimage or createaichatcompletion is executed in an ephemeral V8 sandbox. Your prompts and generated image assets are securely streamed back to your Google Cloud environment and never cached on Vinkius.

Give your Google ADK agents access to multi-model text, speech, and image tools with CometAPI.

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Connect CometAPI MCP to Google ADK

Create your Vinkius account to connect CometAPI 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 CometAPI with Google ADK

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Run CometAPI tools inside Google ADK

The CometAPI MCP Server connects your Google ADK agents to an aggregated suite of multi-provider generation tools. Your agent can ingest massive documents from BigQuery and then use `create_ai_chat_completion` to cross-reference outputs against other models. This hybrid approach lets you process massive enterprise datasets on Google Cloud while using the best model for specific sub-tasks.

Generate media assets directly from your Google Cloud data

Your Google ADK agent can trigger creative tasks based on database updates or user events. By calling `generate_ai_image` or `convert_text_to_speech`, your enterprise agent turns raw structured data into production-ready media. Because this MCP setup integrates directly with your Python code, you don't need to write custom API wrappers.

Restrict CometAPI tools in enterprise environments

Enterprise deployments require strict access controls, which is why this MCP Server setup supports tool filtering. You can use the optional tool_names filter to expose only safe tools like `check_api_health` or `list_supported_ai_providers` to your LlmAgent. This prevents autonomous agents from running costly operations or generating unauthorized assets.

Setup guide

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

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Common questions about CometAPI MCP in Google ADK

Install the package via pip install google-adk. Then, initialize McpToolset with your Vinkius HTTP endpoint and pass it to your LlmAgent instance.

Yes, your agent can call `get_api_usage_statistics` to read your current usage metrics. This is especially useful for tracking cross-provider costs alongside your Google Cloud BigQuery bills.

Yes, the framework natively supports both transport protocols. For production environments on Google Cloud, we recommend using the Streamable HTTP transport to connect directly to your managed endpoint.

The agent invokes `list_available_ai_models` to inspect the available backend engines. This lets your Google ADK pipeline dynamically decide which model to target for specific translation or generation tasks.

Every request sent to `generate_ai_image` or `create_ai_chat_completion` is executed in an ephemeral V8 sandbox. Your prompts and generated image assets are securely streamed back to your Google Cloud environment and never cached on Vinkius.

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