How to Use the Fera.ai MCP in Google ADK

Q: How does the Google ADK use Fera.ai store metadata?

Your agent can call getaccountinfo to read account parameters and getme to verify API token permissions. This helps the agent confirm it has the right access before running deep analysis.

Q: Can I limit which tools the Gemini agent can access?

Yes. Use the optional toolnames filter in the toolset configuration to restrict exposure. For example, you can expose only getproductrating if you want to block access to customer profiles.

Connect Gemini models to your Fera.ai data using the Google ADK and this MCP Server to analyze reviews.

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Connect Fera.ai MCP to Google ADK

Create your Vinkius account to connect Fera.ai 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 Fera.ai with Google ADK

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Analyze Fera.ai product sentiment at scale

Your Gemini agent uses `get_product_rating` and `list_reviews` to pull thousands of Fera.ai customer opinions into its massive context window. It processes the raw text to find common complaints or praise. Because the Google ADK handles long-context reasoning, your agent can analyze entire Fera.ai product catalogs in one go. It identifies trends that simple keyword searches miss.

Manage multi-store Fera.ai feedback loops

If you manage several brands, your agent uses `list_stores` and `list_external_integrations` to map out your entire Fera.ai network. It keeps track of where reviews are coming from. The agent queries `get_account_info` to check Fera.ai subscription limits and active syncs. This ensures your data pipelines remain active across Shopify, WooCommerce, or custom backends.

Track Fera.ai customer visual uploads

Customers upload photos and videos to show off their purchases. Your agent uses `list_media` to retrieve these Fera.ai assets and check their quality. You can combine this with BigQuery datasets using the Google ADK. This lets your agent correlate high-quality visual Fera.ai reviews with actual sales lifts over time.

Setup guide

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

Use the McpToolset class with the HTTP transport parameters pointing to your Vinkius URL. Pass this toolset directly into your LlmAgent initialization block.

No. This MCP Server is read-focused, exposing tools like list_reviews and get_review. It lets your agent analyze and monitor feedback rather than create or modify it.

Your agent can call get_account_info to read account parameters and get_me to verify API token permissions. This helps the agent confirm it has the right access before running deep analysis.

Yes. Use the optional tool_names filter in the toolset configuration to restrict exposure. For example, you can expose only get_product_rating if you want to block access to customer profiles.

The MCP Server runs in a zero-trust sandbox. Raw customer feedback and rating metrics are fetched directly from the API and passed to your Google Cloud environment over encrypted TLS connections, leaving no persistent logs on intermediate servers.

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