How to Use the DeepOpinion (No-code NLP & Text AI API) MCP in Google ADK

Q: How do I restrict Google ADK to only use specific DeepOpinion (No-code NLP & Text AI API) tools?

You can use the optional toolnames filter when initializing your McpToolset. This allows you to expose only the predict tool while keeping your model list and batch tools hidden from the agent.

Run DeepOpinion NLP models directly on your BigQuery data using the Google ADK and Gemini via this MCP Server.

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Connect DeepOpinion (No-code NLP & Text AI API) MCP to Google ADK

Create your Vinkius account to connect DeepOpinion (No-code NLP & Text AI API) 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 DeepOpinion (No-code NLP & Text AI API) with Google ADK

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Analyze BigQuery text tables using this MCP Server

By exposing the `predict` tool to your agent, Gemini can read records from BigQuery and immediately run sentiment analysis on them. This design connects your enterprise data storage directly to your custom NLP models. The agent handles the query, extracts the text, sends it to DeepOpinion, and updates the database with the results.

Perform high-volume classification with Google ADK

The `predict_batch` tool lets Gemini group thousands of rows from your Cloud Storage and analyze them in batches. The Google ADK manages these heavy workloads by combining Gemini's long-context window with DeepOpinion's batch processing. Your agent holds the context of the entire job while the server does the heavy lifting.

Let Gemini select the right NLP model dynamically

Your agent calls the `list_models` tool to inspect your account and select the exact classifier that matches the incoming text. You do not need to hardcode which model to use for different text types. The agent detects the nature of the incoming document, finds the appropriate custom model, and runs the classification without manual setup.

Setup guide

Set up DeepOpinion (No-code NLP & Text AI API) 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 DeepOpinion (No-code NLP & Text AI API) 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="DeepOpinion (No-code NLP & Text AI API)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DeepOpinion (No-code NLP & Text AI API) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about DeepOpinion (No-code NLP & Text AI API) MCP in Google ADK

Yes, they can. You load the server using the McpToolset class with the Vinkius HTTP URL. The ADK exposes the tools directly to your Gemini-powered agent via the MCP protocol.

You can use the optional tool_names filter when initializing your McpToolset. This allows you to expose only the predict tool while keeping your model list and batch tools hidden from the agent.

Yes. Your agent can query BigQuery using Google Cloud tools, pass the retrieved text to the predict_batch tool, and then write the classified outputs back to your database.

The server supports both Stdio and HTTP transports. For cloud-deployed enterprise agents, the Streamable HTTP transport is the recommended way to connect to Vinkius.

All text payloads and model metadata processed by the MCP Server are isolated within an ephemeral V8 sandbox. No data is stored on Vinkius, and transport-level encryption secures the transit between Google Cloud and the API.

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