How to Use the BrandMentions MCP in Google ADK

Q: Can I use BrandMentions with Google ADK for automated BigQuery ingestion?

Yes. Your Gemini agent can fetch social data using getmentions and then use native Google Cloud tools to write those records directly into your BigQuery tables for long-term storage.

Q: How does Google ADK handle the long-running searches from BrandMentions?

Your agent triggers a search job with postsearch and can safely retrieve partial, live results using getprocessedmentions while waiting for the full dataset to compile.

Feed real-time social listening data directly to your Google ADK pipelines to power enterprise Gemini agents.

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Connect BrandMentions MCP to Google ADK

Create your Vinkius account to connect BrandMentions 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 BrandMentions with Google ADK

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Connect BrandMentions to your Google ADK pipelines

This MCP Server connects real-time social listening data directly to your Gemini agents running on Google Cloud. By calling `get_project_mentions` and `list_projects`, your agents can gather massive datasets of brand discussions and dump them straight into BigQuery for deep SQL analysis. Setting this up is straightforward. You wrap the server in a toolset and pass it to your agent, giving your models direct access to track brand health without manual exports or custom pipeline code.

Analyze thousands of social mentions using Gemini

Gemini's million-token context window is perfect for digesting huge piles of social data. Our MCP server lets your agent run `post_search` to start an on-the-spot search, pull the entire dataset with `get_mentions`, and analyze the whole batch in a single prompt. You can also extract key creators using `get_influencers` to map out your brand's digital footprint. The ADK toolset lets your agent handle these complex multi-step research tasks autonomously inside your Google Cloud environment.

Run real-time social searches securely

Sometimes you need immediate answers about a sudden PR crisis. Your agent can spin up a search job with `post_search` and poll `get_processed_mentions` to get quick, early insights before this MCP integration completes the full scan. To keep your cloud budgets in check, your agent can monitor API usage via `get_remaining_credits`. It allows you to build smart enterprise loops that automatically pause search tasks if your operational limits are close to being breached. You can also clean up old campaigns using `delete_project` and spin up new ones using `add_project`.

Setup guide

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

You initialize the toolset using the streamable HTTP parameters pointing to your Vinkius endpoint, and then pass it to your agent constructor. The agent automatically registers all tools like `get_project_mentions`.

Yes. Your Gemini agent can fetch social data using `get_mentions` and then use native Google Cloud tools to write those records directly into your BigQuery tables for long-term storage.

Your agent triggers a search job with `post_search` and can safely retrieve partial, live results using `get_processed_mentions` while waiting for the full dataset to compile.

Yes. The ADK toolset allows you to pass an optional list of allowed tool names, so you can block access to destructive tools like `delete_project` if your agent only needs read access.

Your brand tracking data and API credentials are kept strictly isolated. Vinkius runs the integration in a secure, ephemeral sandbox where no search queries or social mentions are written to persistent storage, maintaining your enterprise security standards.

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