How to Use the Massive MCP in Google ADK

Feed historical dividend records directly into your Google ADK enterprise workflows using this MCP Server.

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Connect Massive MCP to Google ADK

Create your Vinkius account to connect Massive 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 Massive with Google ADK

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Query dividend history directly from Google ADK

The `list_dividends` tool lets your Google ADK agent query historical cash distributions for any ticker directly inside your enterprise workflow. This eliminates the manual step of exporting stock data to CSVs before running your analysis. The Google ADK agent processes the raw distribution dates and payout values, feeding them straight into your Gemini models. You get instant access to historical yields without leaving your secure Google ADK environment.

Combine BigQuery datasets with live MCP Server lookups

The `list_dividends` tool works alongside your existing BigQuery tables to enrich historical stock records on the fly through this MCP Server inside Google ADK. Your agent pulls your custom portfolio list from Google Cloud, then runs `list_dividends` to get the latest distribution numbers. Merging static enterprise data with fresh dividend histories happens in a single Google ADK execution step. Your agent outputs fully updated yield calculations directly into your cloud storage buckets.

Feed dividend histories to long-context Gemini models

Your Google ADK agent uses the `list_dividends` tool to pull decades of distribution records and feeds the entire raw payload into Gemini's massive token window. The model analyzes long-term payout trends without losing context or requiring chunking. This Google ADK setup allows the agent to spot multi-decade dividend growth patterns that shorter-context models miss entirely. You get deep historical insights processed in a single reasoning step.

Setup guide

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

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Common questions about Massive MCP in Google ADK

You initialize the toolset using the streamable HTTP server parameters pointing to your endpoint, then pass it to your agent's tool list. The agent automatically exposes the `list_dividends` tool to your Gemini model.

Yes, you can use the tool names filter parameter during initialization to only expose `list_dividends`. This keeps your agent's prompt clean and prevents it from trying to call unauthorized actions.

Absolutely, the server runs in a zero-trust sandbox that fits right into your Google Cloud security model. Your agent communicates securely over HTTPS using a single, managed authorization token.

When the server returns an empty list for a ticker, your agent detects the null response and can log it to Vertex AI. This prevents your pipeline from crashing on non-dividend-paying stocks.

The stock tickers and historical dividend records processed by the server are never written to persistent storage. All data stays within your ephemeral execution environment and is destroyed the moment the tool execution finishes.

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