How to Use the Formbricks MCP in Google ADK

Q: How does Google ADK interact with Formbricks tools?

You use the McpToolset class to register the server. Your agent treats these tools as native functions, allowing it to query survey results as easily as it reads a database table.

Integrate your Google ADK agent with Formbricks to bridge your survey feedback with your BigQuery enterprise data workflows.

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Connect Formbricks MCP to Google ADK

Create your Vinkius account to connect Formbricks 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 Formbricks with Google ADK

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Direct Feedback Ingestion for Google ADK

Your agent pulls raw survey data directly into your infrastructure using `list_responses`. It makes the feedback available for your Gemini models to reason over alongside your BigQuery datasets. This eliminates the need for manual CSV exports. The agent uses `get_survey` to understand the question context, ensuring the analysis it performs is accurate to the user's intent.

Dynamic Survey Generation with Google ADK

Create targeted feedback loops inside your cloud environment. Your agent invokes `create_survey` based on specific triggers identified in your Google Cloud logs. It keeps your feedback process lean. By using `list_surveys` first, the agent checks if a similar survey already exists, preventing duplicate questions from reaching your users.

Scoped Environment Management

Manage your survey infrastructure with total control. The agent uses `get_product_info` to ensure it's operating on the correct production instance before it runs any management tasks. This protects your configuration from drift. It uses `list_tags` to categorize surveys, keeping your account organized as you scale your feedback operations across multiple products.

Setup guide

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

You use the `McpToolset` class to register the server. Your agent treats these tools as native functions, allowing it to query survey results as easily as it reads a database table.

Yes. You can pass a tool name filter to the toolset, limiting the agent to only the functions you want it to perform. This is useful for read-only agent roles.

The server delivers clean, structured JSON responses that fit perfectly into Gemini's context window. Your agent can ingest hundreds of survey responses in one pass.

The server runs in a zero-trust sandbox. It requires a single authentication token, and all data exchange occurs over an encrypted connection managed by the host.

It exposes full survey objects, including question logic and response payloads. The server does not store this data; it only acts as a pass-through for your agent's requests.

Use it with your favorite AI tools

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