How to Use the Buenbit MCP in Google ADK

Link Google ADK to Buenbit to manage your crypto assets using Gemini's long-context reasoning capabilities.

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Connect Buenbit MCP to Google ADK

Create your Vinkius account to connect Buenbit 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 Buenbit with Google ADK

Ask AI about this MCP

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Claude

Perplexity

Market intelligence for Google ADK

Use `get_ticker` to pull pricing data into your Gemini-powered agent. The model can process these numbers alongside your BigQuery data to build complex trading strategies. This MCP Server provides the raw numbers your agent needs to reason about asset movement. It acts as the bridge between your cloud data and the exchange.

Automated asset management

Execute `execute_withdrawal` or `get_deposit_address` via your agent's toolset. This allows your Google Cloud workflow to move funds based on triggers defined in your infrastructure. Your agent maintains strict control over these calls. You can restrict which tools are exposed to the agent using the tool_names filter in your configuration.

Deep auditing with MCP

Query `list_withdrawals` to verify movement of funds across your accounts. Your agent logs these results back into your existing data warehouse. Tracking costs is simple with `get_network_fees`. You can program your agent to wait for lower gas prices before broadcasting any transaction.

Setup guide

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

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Common questions about Buenbit MCP in Google ADK

Absolutely. You initialize a McpToolset with your server URL and pass it to your LlmAgent. It integrates with your existing Google Cloud infrastructure setup.

The server scales to meet your request volume. Since Google ADK handles the transport, you can monitor execution latency through your standard cloud logging tools.

You provide your endpoint token during the initial configuration. The connection is authenticated once, and then the agent manages the tool sessions securely.

It is. Because the server provides a standard interface, your Gemini models running on Vertex AI can call these tools just like any other external function.

Your wallet balances and order history are treated as temporary context. The server environment is locked down and wipes all session data upon disconnection.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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