How to Use the Fairing MCP in Google ADK

Q: Can I restrict which Fairing tools my Google ADK agent uses?

Yes, use the toolnames filter during initialization to limit the agent to only the tools you need. This prevents the agent from accessing unnecessary survey data.

Connect Fairing to your Google ADK pipelines for enterprise-grade customer intelligence on Google Cloud.

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Connect Fairing MCP to Google ADK

Create your Vinkius account to connect Fairing 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 Fairing with Google ADK

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Sync Google ADK with survey insights

Use `get_insights` to pull aggregated feedback directly into your Vertex AI workflows. This allows your agent to correlate survey trends with your existing BigQuery datasets. Your agent processes this data to generate summaries that inform product roadmap decisions. It maps the output of `list_surveys` to understand which customer segments are providing the most actionable feedback.

Automate response analysis with Google ADK

The `get_customer_responses` tool allows your agent to fetch individual feedback logs for specific users. This works within your existing Google Cloud infrastructure to maintain data integrity. Your agent executes `get_response` to verify specific feedback entries before taking action. It turns raw customer input into a structured format that fits your enterprise logic.

Verify survey question structures

Your agent runs `list_questions` to audit the current survey inventory. This prevents the agent from making assumptions about data fields that have since been deprecated. By calling `get_question`, the agent retrieves the exact schema for any survey field. This ensures your downstream data processing remains compatible with the current Fairing configuration.

Setup guide

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

The server connects via a standard HTTP transport that your Google ADK agent consumes as a toolset. This setup keeps your survey data flow separate from your core compute tasks.

Vinkius manages the server in an ephemeral sandbox with zero-trust principles. Your Google ADK agent only ever sees the data requested through the validated MCP tool set.

Yes, use the tool_names filter during initialization to limit the agent to only the tools you need. This prevents the agent from accessing unnecessary survey data.

It provides clean, structured JSON output that Gemini models handle easily. This is ideal for agents that need to ingest large batches of survey responses.

It shares survey question text, customer answer strings, and account metadata. No PII is exposed unless explicitly included in your custom survey questions.

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