How to Use the Modash MCP in Google ADK

Connect Modash influencer analytics to your Google ADK agent infrastructure.

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Connect Modash MCP to Google ADK

Create your Vinkius account to connect Modash 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 Modash with Google ADK

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Influencer research tools for Google ADK agents

Your Google ADK agents can now query social platforms directly. Use `search_instagram` or `search_youtube` to populate your BigQuery tables with relevant creator data. Your agents act on this data immediately. You keep your research inside your existing cloud architecture without manual data entry.

Verification metrics for Google ADK workloads

Validate your influencer list with `get_tiktok_report` or `get_instagram_report`. Your agents check these reports against your internal conversion targets before recommending a partnership. This MCP Server ensures your enterprise agents operate on verified data. You reduce the risk of working with unproven creators by automating the vetting process.

Targeting tools for precise creator matching

Use `list_interests`, `list_languages`, and `list_locations` to define specific segments for your Google ADK agent. It finds exactly who fits your brand profile. Your agent translates these parameters into actionable search queries. This keeps your influencer acquisition focused on your target demographic.

Setup guide

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

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Common questions about Modash MCP in Google ADK

You link the server via HTTP transport to your agent's toolset. The Google ADK agent then treats the Modash functions as native capabilities.

Yes. You can script your agent to iterate through large datasets using the search and report tools. It handles the API calls while you manage the campaign strategy.

You only need the server running at your chosen endpoint. The Google ADK framework handles the rest, allowing your agents to call tools over standard transports.

The data flows directly from the server to your agent's memory. You choose whether to store it in your cloud storage or process it in volatile memory.

The server enforces strict access controls on every request. Your agent only sees the specific influencer profiles you explicitly authorize it to query.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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