How to Use the Appbot MCP in Google ADK

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

Yes. You can apply the toolnames filter when setting up the MCP Server connection. This limits the agent to safe operations like reading topics instead of pulling full user lists.

Pipe Appbot review data into Google ADK to analyze massive volumes of user feedback using Gemini's million-token context window.

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Connect Appbot MCP to Google ADK

Create your Vinkius account to connect Appbot 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 Appbot with Google ADK

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Analyze months of feedback with an MCP Server and Google ADK

Calling `list_reviews` feeds raw app store complaints directly into your Gemini agent. Instead of chunking the data, you can dump thousands of reviews into the massive context window at once to find hidden patterns. The agent cross-references this raw text with `list_topics` to see how user complaints map to the standard categories identified by Appbot AI. It reads everything in a single pass, giving you a massive advantage when diagnosing sudden rating drops.

Connect feedback to BigQuery

Your agent pulls specific account details using `get_account_info` and grabs individual review text via `get_review_details`. It then structures this data and writes it straight into your Google Cloud infrastructure. You define an MCP Server connection using the McpToolset wrapper. The agent handles the transport layer automatically, fetching the exact user complaints you need to join against your existing customer database.

Track custom topics across markets

Passing `list_apps` and `list_countries` lets your agent map your entire portfolio across different geographic regions. It isolates specific localized issues without requiring manual dashboard exports. When you need deeper analysis, the agent runs `get_reviews_by_custom_topic` to pull feedback matching your specific internal tags. It passes these targeted results into Vertex AI for further enterprise analytics.

Setup guide

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

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Why Choose Vinkius

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Appbot MCP in Google ADK

Run pip install google-adk in your environment. Initialize the connection using McpToolset with your Vinkius StreamableHttpServerParameters, then pass it to your LlmAgent.

Gemini processes over a million tokens simultaneously. You can pull massive arrays of raw reviews and custom topics in one go, letting the model read the entire dataset without losing context.

Yes. You can apply the tool_names filter when setting up the MCP Server connection. This limits the agent to safe operations like reading topics instead of pulling full user lists.

The framework supports both Stdio and HTTP transports natively. You just provide the remote URL to the toolset configuration and the agent handles the rest.

The Appbot MCP Server accesses public app store reviews, device types, and custom topic definitions. Vinkius runs the connection through a zero-trust V8 Isolate Sandbox, ensuring your Gemini prompts and BigQuery writes stay entirely separated from other users.

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