How to Use the Zixflow MCP in Google ADK

Q: How does the Google ADK handle complex filtering for Zixflow?

You use listcollectionrecords and pass a detailed JSON filter body. This allows your agent to pull specific slices of data—like transactions from Q3 only—which are then ready for analysis in Vertex AI.

Q: Can Zixflow collections be managed using the Google ADK?

Yes, your agent can call listcollections to see every collection available. It then uses other tools like createcollectionrecord or updatecollectionrecord to manage the data within those collections.

Q: Does Zixflow support listing all available collections with the Google ADK?

The listcollections tool makes it easy. Your agent first lists every collection to map out where data needs to be written or retrieved before running complex analyses.

Q: Can I track Zixflow transactions using the Google ADK?

Yes, the listwallettransactions tool pulls all transaction history. This financial context is ideal for Gemini models that need to hold massive amounts of data (1M+ tokens) during reasoning.

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Connect Zixflow MCP to Google ADK

Create your Vinkius account to connect Zixflow 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 Zixflow with Google ADK

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List and Filter Records with MCP Server

The `list_collection_records` tool lets your agent pull data from Zixflow collections. You feed it a JSON body to sort or filter, which is perfect for querying large datasets residing in BigQuery. Your Gemini model can then reason over those filtered results.

Create and Modify Contacts

When your agent needs new data, use `create_collection_record` to write a record into a specific collection. It can also modify existing entries via `update_collection_record`. This structured process guarantees that the contact details hit BigQuery correctly.

Full Transaction Visibility

Agents need financial context. The `list_wallet_transactions` tool pulls a comprehensive list of Zixflow wallet transactions. You can combine this with record listings to give your agent a full view of the account's financial health.

Setup guide

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

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Common questions about Zixflow MCP in Google ADK

You use `list_collection_records` and pass a detailed JSON filter body. This allows your agent to pull specific slices of data—like transactions from Q3 only—which are then ready for analysis in Vertex AI.

Yes, your agent can call `list_collections` to see every collection available. It then uses other tools like `create_collection_record` or `update_collection_record` to manage the data within those collections.

This server touches collection records, encompassing contact and company details. Because you’re building enterprise agents on Google Cloud, the native integration with BigQuery helps maintain a secure, auditable environment.

The `list_collections` tool makes it easy. Your agent first lists every collection to map out where data needs to be written or retrieved before running complex analyses.

Yes, the `list_wallet_transactions` tool pulls all transaction history. This financial context is ideal for Gemini models that need to hold massive amounts of data (1M+ tokens) during reasoning.

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