How to Use the Birdeye MCP in Google ADK

Q: How does Gemini's long context benefit Birdeye analysis?

It lets you pass months of raw responses from getsurveyresponses into a single prompt. The model can then perform deep thematic analysis on customer feedback without pagination limits.

Analyze Birdeye feedback and trigger surveys using Google ADK agents backed by BigQuery and Gemini's massive context.

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Connect Birdeye MCP to Google ADK

Create your Vinkius account to connect Birdeye 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 Birdeye with Google ADK

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Feed Birdeye reviews into Google ADK pipelines

Your agent runs `list_reviews` to pull customer sentiment directly into your Google ADK context window. This integration allows Gemini models to cross-reference Birdeye feedback with BigQuery tables. This integration allows your Google ADK model to spot regional customer trends. By matching `get_review_summary` outputs with internal store IDs, your agent can flag underperforming locations to regional managers automatically.

Bulk check-ins with 1M token context windows

The agent uses `checkin_customer` to selectively run Birdeye review requests for high-value targets processed by Google ADK. Google ADK lets your agent ingest thousands of rows from BigQuery before triggering the tool. The Gemini model reads entire customer histories in one go before deciding who gets a Birdeye survey. It uses `get_contact` to verify details, ensuring your Google ADK agent never spams active support tickets.

Filter Birdeye MCP Server tools for enterprise safety

You do not have to expose all ten tools, keeping `reply_to_review` hidden from public-facing Google ADK agents. Use the ADK toolset filter to expose harmless Birdeye tools while keeping execution tools locked down. This selective exposure keeps your Google ADK enterprise agents safe. If an agent only needs to read `get_survey_responses`, you simply exclude destructive tools from the toolset initialization.

Setup guide

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

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Common questions about Birdeye MCP in Google ADK

Install the ADK package and initialize McpToolset with your Vinkius HTTP transport URL. Pass that toolset directly into your LlmAgent tools array to expose the customer support functions.

Yes. Because the ADK runs natively in the Google Cloud ecosystem, your agent can query BigQuery for customer IDs, check their status with get_contact, and then trigger a review request.

Yes, it supports both transports. For production cloud deployments, use the HTTP transport with your Vinkius endpoint token to keep your MCP connection stateless and lightweight.

It lets you pass months of raw responses from get_survey_responses into a single prompt. The model can then perform deep thematic analysis on customer feedback without pagination limits.

All survey responses and customer contact details pass through ephemeral, zero-trust MCP isolates. Your customer experience data is never used for model training and remains fully sandboxed during execution.

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