How to Use the Cacheflow MCP in Google ADK

Q: Can Google ADK agents update CRM records using Cacheflow?

Yes, your agent can call synctocrm to update external databases. It matches the proposal data with your BigQuery customer records before writing the final sync, avoiding duplicate accounts.

Deploy enterprise sales agents on Google ADK that connect BigQuery data to Cacheflow billing workflows.

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Connect Cacheflow MCP to Google ADK

Create your Vinkius account to connect Cacheflow 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 Cacheflow with Google ADK

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Run deep deal analysis with Google ADK

Pull historical sales data from BigQuery and feed it directly into your Cacheflow MCP setup. Your Gemini-powered agent uses `list_proposals` to gather raw contract history and analyze patterns over millions of tokens. Because Google ADK supports massive context windows, your agent can compare hundreds of active proposals at once. It identifies bottlenecked deals without losing track of the original pricing parameters.

Trigger CRM updates and customer checks

Keep your enterprise records aligned by letting Gemini handle the data entry. The agent calls `list_customers` to verify account names and then runs `sync_to_crm` to push updates directly to your system of record. This eliminates manual copy-pasting between your Google Cloud databases and your billing platform. Your agent runs these checks in the background, keeping your sales team focused on closing.

Accelerate approvals via this Google ADK MCP Server

Stop waiting for manual deal reviews. Your agent runs `get_approval_requests` to fetch pending quotes and drafts a summary of the terms using `get_proposal_details`. It then prepares a ready-to-send email or chat notification for your finance team. If changes are needed, the agent can quickly rewrite the contract details using `create_proposal`.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cacheflow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cacheflow MCP in Google ADK

Install the ADK and use the McpToolset class with your Vinkius HTTP URL. Pass this toolset to your LlmAgent to expose tools like `create_proposal` and `list_customers` to Gemini. You can also restrict tools using the tool_names filter.

Yes, your agent can call `sync_to_crm` to update external databases. It matches the proposal data with your BigQuery customer records before writing the final sync, avoiding duplicate accounts.

Gemini models can process up to 1 million tokens, allowing your agent to read entire contract histories. It can pull details from `list_proposals` and analyze trends across your entire customer base using this MCP tool.

Yes, this MCP integration supports both connection styles. For cloud deployments on Vertex AI, the Streamable HTTP transport is the easiest way to keep your agent connected.

Your proposals and customer lists are processed entirely within your Google Cloud security perimeter. The ADK never sends your billing data or CRM syncs to external servers, keeping your financial metrics private.

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