How to Use the AppFollow MCP in Google ADK

Q: Can I filter AppFollow tools in Google ADK?

You can. Use the toolnames filter to expose only the specific functions your agent needs. This keeps your toolset clean and focused on your specific Google Cloud infrastructure.

Connect AppFollow to your Google ADK pipelines for automated store intelligence.

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Connect AppFollow MCP to Google ADK

Create your Vinkius account to connect AppFollow 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 AppFollow with Google ADK

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Google ADK store intelligence tools

Use `get_rankings` to pull current store positions into your Gemini-powered workflows. Your agent processes these figures alongside your existing BigQuery data. `get_ratings_history` provides the raw numbers needed for long-context reasoning. You can now correlate store performance with your internal business metrics.

Summarize user feedback via Google ADK

Run `get_reviews_summary` to get the current average rating and review volume. Your agent uses this to flag drops in quality before they hit your dashboard. `get_reviews_ai_summary` pulls the latest user feedback into your context window. It turns raw text into actionable insights for your support team.

Verify app connection status

Execute `get_account_check` to ensure your agent has valid access to your AppFollow data. It's a quick way to confirm your connection remains active. `get_app_info` fetches the latest details for your projects. You keep your agent synced with the latest store information at all times.

Setup guide

Set up AppFollow 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 AppFollow 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="AppFollow_agent",
    model="gemini-2.0-flash",
    instruction="You have access to AppFollow 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 AppFollow. 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.

Why Choose Vinkius

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Common questions about AppFollow MCP in Google ADK

Yes, our server uses the McpToolset interface compatible with Google ADK. You define the server parameters and the agent handles the rest through the standard protocol.

You can. Use the tool_names filter to expose only the specific functions your agent needs. This keeps your toolset clean and focused on your specific Google Cloud infrastructure.

Your agent can fetch review text and ranking data as long as you provide the endpoint token. The integration supports both Stdio and HTTP transports to fit your cloud architecture.

Gemini models can store massive amounts of review history fetched by our server. You can process entire months of user feedback in a single pass.

They are. We store your authentication tokens in an ephemeral V8 sandbox. No other process can touch your AppFollow credentials while your agent is running.

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