How to Use the Determ MCP in Google ADK

Q: Can Google ADK restrict which Determ tools the agent sees?

You can pass a toolnames filter when setting up the toolset. This prevents the agent from calling tools like getaccountmetadata if you only want it reading mentions.

Feed global media mentions straight into Gemini using Google ADK and Determ.

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Connect Determ MCP to Google ADK

Create your Vinkius account to connect Determ 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 Determ with Google ADK

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Massive context media analysis via MCP Server

Gemini models use the `list_media_mentions` tool to pull raw PR hits directly into their massive context windows. By hooking up this MCP Server to Google ADK, your agent can ingest thousands of articles at once. It reads the entire news cycle in one pass. You do not have to write manual pagination logic. The agent uses `search_mentions_by_keyword` to find highly specific brand conversations. It dumps the results straight into Gemini's 1M+ token window for deep thematic analysis.

Connect PR data to Google Cloud

Enterprise teams use the `list_top_media_sources` tool to pull publication rankings and structure that data for BigQuery. Your Google ADK agent creates a direct pipeline from external news to internal analytics. You stop copying and pasting metrics. When a major news cycle hits, the agent runs `get_mention_details` to extract the full article text and metadata. It then formats this payload for Vertex AI pipelines. You can easily cross-reference external media hits with your internal sales metrics.

Monitor brand health automatically

Keeping track of public opinion requires the `get_monitoring_query_details` tool to check the status of your active Determ trackers. Your Gemini agent knows exactly which topics are currently active. It flags the ones that need updating. The agent then calls `get_query_sentiment_summary` to grab the positive, neutral, and negative breakdowns. Instead of just plotting a chart, Gemini reads the shifts in sentiment. It writes an executive summary explaining exactly why the changes occurred.

Setup guide

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

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Common questions about Determ MCP in Google ADK

Install google-adk and create an McpToolset using the StreamableHttpServerParameters class. Pass this toolset directly to your LlmAgent configuration to initialize this MCP Server.

You can pass a tool_names filter when setting up the toolset. This prevents the agent from calling tools like get_account_metadata if you only want it reading mentions.

The server fetches the data, and your Google ADK agent handles the routing. You can easily write a custom tool that takes the output from list_recent_high_reach_mentions and inserts it into a BigQuery table.

Gemini's massive context window can process hundreds of mentions simultaneously. The agent pulls the data through the connection and reasons across the entire dataset without losing detail.

The server processes public media mentions, author names, and publication URLs. Vinkius operates on a zero-trust architecture, so your data passes through an ephemeral sandbox that destroys itself after the request finishes.

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