How to Use the Upstream Lens MCP in Google ADK

Analyze massive oil & gas datasets using the Google ADK for deep analytics.

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Connect Upstream Lens MCP to Google ADK

Create your Vinkius account to connect Upstream Lens 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 Upstream Lens with Google ADK

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Pull and cross-reference project data

You can use `list_projects` to get a list of all environmental projects. Since this is running through the Google ADK, you'll be able to feed that entire dataset into your Gemini context window. If you need notes, calling `list_project_notes` lets your agent pull updates and then perform long-context reasoning across those records.

Manage portfolio data in BigQuery

The tool `list_portfolios` pulls all available portfolios for analysis. Because the Google ADK integrates with BigQuery, this list can be immediately queried against massive historical production datasets. This lets you build complex reports that wouldn't fit into a standard chat interface.

Get specific property and imagery details

To check out asset specifics, run `get_property_details`. Your agent can then use the resulting data structure to pull all related images via `list_property_imagery`. This whole process is designed for Google Cloud, so everything stays within your enterprise infrastructure.

Setup guide

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

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Common questions about Upstream Lens MCP in Google ADK

The Google ADK treats the MCP Server as a native toolset. Your Gemini models can execute calls like `list_project_observations` and treat the returned data just like it came from BigQuery, keeping everything in one place.

Yes. The ADK is built for enterprise agents acting on cloud resources. It supports passing the MCP Server as a toolset, letting you access its full suite of data analytics tools.

You can run multi-step workflows—for example, first getting organization info with `get_organization_info`, and then using that metadata to filter project notes. The long context of Gemini handles the whole chain.

It exposes operational reports, including detailed observations from `list_project_observations` and core metadata about properties. These are structured for deep querying within the Google Cloud environment.

Because this MCP Server touches environmental project notes, property imagery, and organizational metadata, ensure your agent's permissions are scoped correctly within BigQuery.

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