How to Use the Cometly MCP in Google ADK

Q: Can I restrict which Cometly tools Google ADK can access?

Yes, you can use the optional toolnames filter when initializing the toolset. This lets you expose only tracking tools like tracklead while hiding campaign management tools.

Q: How does Google ADK combine Cometly data with BigQuery?

Your agent can pull campaign performance via getcampaignstats and write those metrics directly to BigQuery. This lets you merge attribution data with your internal warehouse records.

Run enterprise marketing analysis using Google ADK to query Cometly attribution data directly.

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Connect Cometly MCP to Google ADK

Create your Vinkius account to connect Cometly 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 Cometly with Google ADK

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Query Cometly ad accounts inside Google ADK

The `list_ad_accounts` tool exposes all connected ad channels directly to your Gemini-powered enterprise agents. This allows your agent to identify active channels and cross-reference them with external BigQuery datasets. By executing `list_campaigns`, the agent grabs the active campaign register in real-time. It maps your marketing architecture directly within your Google Cloud ecosystem.

Feed attribution data to long-context models

The `list_contacts` tool pulls complete customer journey histories into Gemini's massive one-million-token context window. This lets your agent analyze deep historical patterns across thousands of attributed leads. Combined with `list_events`, your agent processes raw conversion timelines without running out of memory. It detects complex multi-touch attribution paths that shorter-context models miss.

Register conversions via MCP Server

The `track_event` tool logs custom conversion events to your Cometly dashboard straight from Google Cloud functions. Your agent monitors backend systems and fires attributions instantly. Using `track_purchase` and `track_lead` ensures your paid search and social campaigns get credited immediately. It connects your cloud database triggers directly to your marketing attribution engine.

Setup guide

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

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Common questions about Cometly MCP in Google ADK

Install the package via pip install google-adk and define your server using McpToolset with the Vinkius HTTP URL. Pass the toolset directly into your LlmAgent tools array.

Yes, you can use the optional tool_names filter when initializing the toolset. This lets you expose only tracking tools like track_lead while hiding campaign management tools.

Your agent can pull campaign performance via get_campaign_stats and write those metrics directly to BigQuery. This lets you merge attribution data with your internal warehouse records.

Yes, the transport layer supports standard HTTP and Stdio configurations. You can run these agents inside Cloud Run or Vertex AI notebooks.

All ad spend and conversion metrics are handled via sandboxed V8 execution environments. Vinkius uses secure MCP gateways to process your marketing requests, ensuring zero persistent storage of your financial data.

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