How to Use the Belvo MCP in Google ADK

Q: How do I configure the Belvo MCP Server with Google ADK?

Initialize a McpToolset with StreamableHttpServerParameters pointing to your Vinkius endpoint. Pass that toolset directly to your LlmAgent instance.

Q: Can Google ADK write Belvo data to BigQuery?

Yes, your agent can route the output from listincomes straight into your data warehouse. You handle the database connection while the MCP server handles the API extraction.

Q: Does Google ADK support filtering Belvo tools?

You can pass an optional toolnames array when setting up the toolset. This restricts the agent to specific endpoints like getlink instead of exposing the entire catalog.

Q: How does the 1M token window help with banking data?

You can dump massive JSON arrays from listtransactions directly into the prompt. The agent reads the entire financial history without requiring vector databases or complex chunking logic.

Bring LATAM banking data into Google ADK for enterprise agents.

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Connect Belvo MCP to Google ADK

Create your Vinkius account to connect Belvo 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 Belvo with Google ADK

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Feed banking data into Gemini.

`list_accounts` and `get_account` pull structured bank details directly into your Google ADK environment. You pass these JSON payloads straight into Gemini's massive context window. The model can ingest thousands of banking records at once. It reads the account structures and cross-references them with your existing BigQuery datasets to spot regional trends.

Analyze massive transaction histories.

`list_transactions` and `get_transaction` extract years of user spending data from Latin American banks. Your agent pulls this raw feed and maps it against Google Cloud infrastructure using the MCP standard. Because Gemini handles millions of tokens, you don't need to chunk the transaction logs. The agent processes the entire financial history in a single prompt to identify long-term spending patterns.

Connect the Belvo MCP Server for wealth tracking.

`get_investments` and `list_owners` expose portfolio holdings and account ownership details. Your enterprise agent uses these tools to build detailed wealth profiles. Google ADK lets you restrict these specific tools using the `tool_names` filter on the `McpToolset` class. This keeps your agent focused on wealth management instead of getting distracted by basic checking accounts.

Setup guide

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

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Common questions about Belvo MCP in Google ADK

Initialize a `McpToolset` with `StreamableHttpServerParameters` pointing to your Vinkius endpoint. Pass that toolset directly to your `LlmAgent` instance.

Yes, your agent can route the output from `list_incomes` straight into your data warehouse. You handle the database connection while the MCP server handles the API extraction.

You can pass an optional `tool_names` array when setting up the toolset. This restricts the agent to specific endpoints like `get_link` instead of exposing the entire catalog.

You can dump massive JSON arrays from `list_transactions` directly into the prompt. The agent reads the entire financial history without requiring vector databases or complex chunking logic.

The server processes highly confidential recurring expense data and income insights. All requests route through a zero-trust, ephemeral environment that requires a single endpoint token for authentication.

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