How to Use the Brankas MCP in Google ADK

Q: How do I connect the Brankas toolset to Google ADK?

Install the SDK with pip install google-adk and define your McpToolset using the Vinkius HTTP URL. Pass this toolset into the tools list when initializing your LlmAgent to expose the banking functions.

Q: Can I restrict a Google ADK agent to only read Brankas balances?

Yes, you can use the toolnames filter in the configuration to expose only balance and identity tools. This prevents the agent from accessing transfer or checkout tools entirely.

Run enterprise financial operations in Google ADK using Gemini models to process Brankas bank statements.

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Connect Brankas MCP to Google ADK

Create your Vinkius account to connect Brankas 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 Brankas with Google ADK

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Analyze multi-month Brankas statements with Google ADK

This integration uses the `get_statement` tool to feed raw transactional history directly into Gemini's million-token context window. The agent parses thousands of lines of bank statements without hitting context limits or losing track of historical trends. Because Google ADK integrates with your cloud storage, you can have the agent pull the statement, find anomalies, and write the structured results directly to a BigQuery table for your finance team.

Manage Brankas checkout pipelines in Google ADK

This feature uses `create_checkout` and `get_transaction` to initiate direct bank payments and verify their completion. Your Gemini-powered agent generates the checkout link and monitors the transaction lifecycle. The ADK framework allows you to filter which tools are exposed using the `tool_names` parameter. This ensures your customer-facing agents can only run checkout queries, keeping disbursement tools locked down.

Execute secure bank payouts via this MCP Server

This operation uses `inter_bank_transfer` and `intra_bank_transfer` alongside `get_transfer_status` to run batch payouts. Your agent reads recipient lists from Google Cloud databases and executes the transfers. By running this integration on Vinkius, your Google ADK agent gets a single, authenticated endpoint to communicate with local banks, avoiding complex credential management inside your Vertex AI pipelines.

Setup guide

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

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Common questions about Brankas MCP in Google ADK

Install the SDK with `pip install google-adk` and define your `McpToolset` using the Vinkius HTTP URL. Pass this toolset into the `tools` list when initializing your `LlmAgent` to expose the banking functions.

Yes, you can use the `tool_names` filter in the configuration to expose only balance and identity tools. This prevents the agent from accessing transfer or checkout tools entirely.

The ADK leverages Gemini's long-context window to ingest the entire output of the statement tool in a single call. This lets the agent run complex financial analysis without needing chunking or vector search.

Yes, the framework supports both transports, but for cloud-deployed agents, you should use the Streamable HTTP transport with your secure Vinkius endpoint token.

Balance data retrieved from local banks is processed in memory within your VPC boundary on Google Cloud. The Vinkius MCP execution environment ensures no financial data is used to train Google's foundation models, and all transmission is encrypted.

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