How to Use the ComboCurve MCP in Google ADK

Q: Can I combine ComboCurve data with BigQuery using Google ADK?

Yes. Your agent can pull well data using getwell and query BigQuery in the same execution loop to join live forecasts with historical tables.

Run deep oil and gas analytics by connecting Google ADK agents to your ComboCurve forecasts.

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Connect ComboCurve MCP to Google ADK

Create your Vinkius account to connect ComboCurve 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 ComboCurve with Google ADK

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Enterprise Well Auditing with Gemini

`list_wells` and `get_well` pipe raw asset parameters directly into Gemini's massive one-million-token context window. Your agent scans thousands of wells to flag anomalies or missing attributes instantly. This setup lets you cross-reference your physical asset database with external Google Cloud storage. The agent handles the data mapping without manual script writing.

BigQuery Mapping via Google ADK MCP Server

This MCP Server connects your live ComboCurve database to your enterprise data warehouse. Your agent triggers `get_monthly_volumes` to compare active forecasts against historical production tables in BigQuery. You configure the connection using the toolset class in Python. The agent uses native Google Cloud integrations to write these comparisons back to your central database.

Scenario Inspection

`list_scenarios` and `get_scenario_details` retrieve economic run configurations directly. Your agent parses these complex structures to summarize project economics for your team. By feeding these outputs directly to Vertex AI, you get fast, automated summaries of your running cases. You don't have to export CSVs or click through web interfaces.

Setup guide

Set up ComboCurve 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 ComboCurve 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="ComboCurve_agent",
    model="gemini-2.0-flash",
    instruction="You have access to ComboCurve 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 ComboCurve. 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 ComboCurve MCP in Google ADK

Use the toolset class with your Vinkius HTTP URL. Pass that toolset directly to your agent constructor to expose the database tools to Gemini.

Yes. Your agent can pull well data using `get_well` and query BigQuery in the same execution loop to join live forecasts with historical tables.

Gemini's long-context window easily digests the massive JSON payloads returned by `get_monthly_volumes`. The framework streams the tool outputs directly into the model's active memory.

Yes. Use the optional tool name filter in your toolset configuration to expose only `search_wells_by_name` and block the forecast tools.

Yes. The Vinkius runtime executes the server in a secure, ephemeral sandbox. Your proprietary well logs and economic scenarios are never stored or used for model training.

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