How to Use the UserStack User-Agent Lookup MCP in Google ADK

Q: How does UserStack User-Agent Lookup work with Google ADK?

The agent invokes detectuseragent via the MCP toolset, passing a UA string. The resulting device, browser, and OS data is then available for reasoning within your Google Cloud workflow.

Q: Can I use UserStack User-Agent Lookup in my Google ADK projects?

Yes. You simply pass the MCP toolset containing UserStack User-Agent Lookup to the LlmAgent, making its capabilities available for your enterprise workflow.

Get User-Agent details for enterprise agents on Google ADK.

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Connect UserStack User-Agent Lookup MCP to Google ADK

Create your Vinkius account to connect UserStack User-Agent Lookup 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 UserStack User-Agent Lookup with Google ADK

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Analyze Client Context with MCP Server

The `detect_user_agent` tool reads a raw User-Agent string and breaks it down into device, browser, and OS components. This structured data is crucial for feeding context to your long-context Gemini models. You can use this output to segment users before running complex queries against BigQuery. The agent knows *who* it's talking to before it writes the SQL.

Run MCP Server Tools on Google ADK

By passing the `UserStack User-Agent Lookup` toolset, your LlmAgent can use context from the UA lookup alongside data in BigQuery. This lets you reason about user behavior and system requirements simultaneously. It’s a powerful combination for enterprise agents; they don't just answer questions—they validate the environment first.

Deep Data Reasoning with MCP Server

You can feed the results of `detect_user_agent` into Gemini to perform high-level reasoning. For instance, if a user is on an older OS, the agent knows it needs to adjust its output format accordingly. This tool enables your agents to handle millions of tokens of context while keeping specific client constraints in mind.

Setup guide

Set up UserStack User-Agent Lookup 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 UserStack User-Agent Lookup 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="UserStack User-Agent Lookup_agent",
    model="gemini-2.0-flash",
    instruction="You have access to UserStack User-Agent Lookup tools via MCP.",
    tools=mcp_tools,
)

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Common questions about UserStack User-Agent Lookup MCP in Google ADK

The agent invokes `detect_user_agent` via the MCP toolset, passing a UA string. The resulting device, browser, and OS data is then available for reasoning within your Google Cloud workflow.

Yes. You simply pass the MCP toolset containing `UserStack User-Agent Lookup` to the LlmAgent, making its capabilities available for your enterprise workflow.

It delivers structured context: device type, browser family, and operating system. This information acts as a vital input variable when querying or processing BigQuery results.

Absolutely. The structured output from the UA lookup is small enough to be managed alongside massive context windows, supporting robust decision-making.

The server processes the raw User-Agent string. The resulting output passed through your agent is limited to device, browser, and OS type, ensuring necessary context without revealing sensitive PII.

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