How to Use the MRPLN MCP in Google ADK

Q: How do I add MRPLN tools to a Google ADK agent?

You initialize an McpToolset with the server URL and pass it into your LlmAgent. It’s a standard approach that works with both Stdio and HTTP transports.

Q: Can I filter which MRPLN tools my Google ADK agent sees?

Absolutely. Use the toolnames filter when setting up your toolset to restrict the agent to only what it needs for the specific task.

Integrate MRPLN with Google ADK to power your enterprise agents with real-time production and customer data.

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Connect MRPLN MCP to Google ADK

Create your Vinkius account to connect MRPLN 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 MRPLN with Google ADK

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Sync production data in Google ADK

Use `list_customers` to pull your entire database into your agent's context. It’s built for large-scale operations where you need to verify records fast. Once you have the list, use `get_customer` to zero in on specific accounts. Your agent will have the full picture before it makes a single move.

Control messaging tools via Google ADK

Your agent can send status updates using `send_email_message` or `send_sms_message` whenever a production milestone shifts. It keeps your procurement team in the loop automatically. If you need to send a formatted alert, `send_whatsapp_message` is available. It processes your components as a JSON string to keep your templates clean.

Track metrics with Google ADK

Query your marketing and production efforts with `list_tactics`. It provides the baseline your agent needs to understand what is working on the floor. Follow up with `get_tactic_performance` to see the actual numbers. You’ll know exactly which tactics are hitting targets and which ones need a pivot.

Setup guide

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

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Common questions about MRPLN MCP in Google ADK

You initialize an `McpToolset` with the server URL and pass it into your `LlmAgent`. It’s a standard approach that works with both Stdio and HTTP transports.

Absolutely. Use the tool_names filter when setting up your toolset to restrict the agent to only what it needs for the specific task.

Yes, but be mindful of the data you pull. Fetching large lists via `list_customers` will consume tokens, so request only the fields you need.

Check your Google Cloud logs for the agent process. If a call fails, the response from the server will be logged as a standard exception.

Vinkius uses a zero-trust architecture. Your production schedules and customer contact info are processed in an isolated sandbox, ensuring no leakage across your cloud infrastructure.

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