How to Use the Arrivy MCP in Google ADK

Q: How do I configure the Arrivy MCP Server inside a Google ADK project?

Instantiate McpToolset with your Vinkius HTTP URL. Pass this toolset into the tools list when initializing your LlmAgent in Python.

Q: Can I limit which Arrivy tools my Google ADK agent can access?

Yes. Use the optional toolnames filter in your McpToolset configuration to expose only specific tools like createtask while hiding administrative tools.

Q: How does Google ADK handle the long-context analysis of Arrivy tasks?

Because Gemini supports huge context windows, you can call listtasks to fetch hundreds of records and feed them directly into the model without running out of memory.

Run enterprise field service routing and task updates using Google ADK with Gemini and managed MCP tools.

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Connect Arrivy MCP to Google ADK

Create your Vinkius account to connect Arrivy 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 Arrivy with Google ADK

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Analyze historical service data with Gemini

`list_tasks` pulls your entire service history into Gemini's million-token context window via the Google ADK. Your agent reads months of field records to spot scheduling bottlenecks. By combining this MCP tool with your BigQuery datasets, your Google ADK agent cross-references past drive times with real-time field performance. This gives you a clear picture of field efficiency.

Assign Crews via Google ADK MCP Server

`list_crews` exposes your active field teams to Gemini so your Google ADK agent can assign tasks. The agent reads live GPS locations and matches them against database records in Vertex AI. This eliminates manual dispatch screens by letting the LLM coordinate crew availability directly through the Vinkius MCP gateway. You get automated dispatching without writing custom glue code.

On-demand customer location mapping

`list_locations` feeds geographic coordinates from Arrivy into Google Cloud's mapping tools. Your agent processes these locations to optimize route density for the field crews. You configure this in your Python code by wrapping the server URL in a Google ADK toolset, making the spatial data immediately actionable. This lets you build real-time routing engines directly on Vertex AI.

Setup guide

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

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Common questions about Arrivy MCP in Google ADK

Instantiate `McpToolset` with your Vinkius HTTP URL. Pass this toolset into the `tools` list when initializing your `LlmAgent` in Python.

Yes. Use the optional `tool_names` filter in your `McpToolset` configuration to expose only specific tools like `create_task` while hiding administrative tools.

Because Gemini supports huge context windows, you can call `list_tasks` to fetch hundreds of records and feed them directly into the model without running out of memory.

Yes, but for cloud-deployed agents on Vertex AI, the Streamable HTTP transport hosted by Vinkius is the standard way to connect this MCP Server.

All crew names and GPS coordinates remain within your Google Cloud boundary and the ephemeral Vinkius sandbox. No task details are written to persistent storage during the execution of these tools.

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