How to Use the Feathery MCP in Google ADK

Q: Can I filter Feathery tools in Google ADK?

Yes. Use the toolnames filter to restrict which functions your agent can execute. This keeps your agent focused on specific tasks like form management.

Bring Feathery form data into your Google ADK agent workflows on Google Cloud.

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Connect Feathery MCP to Google ADK

Create your Vinkius account to connect Feathery 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 Feathery with Google ADK

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Connect Feathery to Google ADK

Use the McpToolset to plug this MCP Server into your LlmAgent. Your agents gain immediate access to `get_account_info` and `list_forms` without extra boilerplate. It works natively with your existing Google Cloud infrastructure. Your agents can now reason over Feathery data alongside your BigQuery assets.

Run complex form logic in Google ADK

Your agents can now fetch user-specific data using `get_user_data` or check specific workflow states with `get_workflow_details`. This turns your agent into an active participant in your business operations. It acts on the data immediately, reducing the need for manual intervention.

Audit Feathery state within Google ADK

Use `list_connector_logs` to keep tabs on your integrations. Your agents can monitor these logs to detect issues in your automated workflows. It’s a direct way to maintain visibility. Your agent flags errors the moment they appear in the logs.

Setup guide

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

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Common questions about Feathery MCP in Google ADK

Install the toolset package and initialize it with your server URL. You then pass the toolset to your LlmAgent to start calling functions.

Yes. Your agents can ingest large quantities of form data and session logs, allowing them to reason across your entire Feathery account state.

Yes. Use the tool_names filter to restrict which functions your agent can execute. This keeps your agent focused on specific tasks like form management.

It does. You can choose between Stdio or HTTP transports depending on your specific Google Cloud deployment environment.

This server uses scoped credentials to access form submissions and user logs. You define exactly what the agent sees, keeping your sensitive user data isolated.

Use it with your favorite AI tools

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