How to Use the DataScope MCP in Google ADK

Q: How do I restrict which DataScope tools my agent sees?

Use the optional toolnames filter in your toolset configuration. This prevents the model from attempting to pull PDFs if you only want it analyzing raw text submissions.

Feed offline inspection data into Google ADK to analyze massive field operations with Gemini.

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Connect DataScope MCP to Google ADK

Create your Vinkius account to connect DataScope 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 DataScope with Google ADK

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Map DataScope forms to BigQuery

The `list_available_forms` tool exposes your entire catalog of mobile inspection templates to the Gemini model. Agents read these schemas to understand exactly what field data is coming in. Moving those answers into Google Cloud becomes trivial. You ingest massive historical datasets using the 1M+ token window, correlating form structures with your existing enterprise data warehouses.

Analyze field operations via MCP Server

Deep inspection context flows through the `get_submissions_with_metadata` tool. This operation grabs timestamps, sync delays, and user inputs from offline audits the moment devices reconnect to the network. Gemini thrives on this kind of dense, structured information. The model cross-references metadata against historical Vertex AI patterns to predict equipment failures before the next audit cycle.

Track workforce spatial patterns

Pinpointing your team requires the `list_tracked_locations` tool to pull GPS pings from the field. Agents map these points alongside `list_organization_users` to verify coverage across remote work sites. Combining spatial coordinates with raw submission data gives you a complete picture of operations. The MCP connection feeds this directly into your Google Cloud infrastructure for real-time dashboarding.

Setup guide

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

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Common questions about DataScope MCP in Google ADK

Run pip install google-adk to start. Initialize an McpToolset with StreamableHttpServerParameters pointing to your Vinkius URL, then pass it to your LlmAgent tools array.

The framework passes your exact parameters to the server. You can filter by form ID or user ID when calling the submissions tool to narrow down the context window.

Both Stdio and HTTP transports work out of the box here. Vinkius provides a hosted HTTP endpoint so your Gemini agents connect without running local services.

Use the optional tool_names filter in your toolset configuration. This prevents the model from attempting to pull PDFs if you only want it analyzing raw text submissions.

Employee locations and audit metadata stay protected behind ephemeral Vinkius auth. The zero-trust architecture guarantees your Gemini pipeline only touches the specific GPS pings and form answers you explicitly request.

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