How to Use the MonkeyLearn MCP in Google ADK

Q: How do I initialize the MonkeyLearn MCP server in Google ADK?

Run pip install google-adk and create an instance of McpToolset using the StreamableHttpServerParameters pointing to your Vinkius endpoint. Pass this toolset directly into the tools list of your LlmAgent.

Q: Can I restrict which MonkeyLearn tools my Google ADK agent can access?

Yes, you can pass a list of allowed tool names to your McpToolset constructor. This limits your agent to specific operations like classifytext while hiding administration tools.

Q: How do I inspect the details of an active classifier?

Execute getclassifierdetails to retrieve the active tag list and model information. This provides your agent with the exact categories it should expect from classification runs.

Feed structured text insights directly into your Google ADK pipelines for enterprise-scale analysis.

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Setup MonkeyLearn with Google ADK

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Run text classification inside your Google ADK loops.

The `classify_text` tool processes massive text fields to assign categories, sentiment, or custom labels. Your Google ADK agent calls this tool to process large text strings retrieved from your active BigQuery tables. This integration lets you run high-throughput NLP classification without exporting your data to external environments first. Your Gemini models use these structured outputs to make decisions over long conversations.

Extract key entities using Google ADK tools.

The `extract_text` tool pulls specific features, keywords, and names out of long-form documents. Google ADK agents use these extracted blocks to build clean metadata profiles for storage in Vertex AI vector databases. By isolating the extraction step, you reduce the token overhead of your primary reasoning model. The agent receives only the relevant text fragments instead of digesting thousands of redundant words.

Monitor your MCP Server pipeline configurations.

The `list_pipelines` tool retrieves the active text processing pipelines configured in your workspace. Your Google ADK agent queries this metadata to ensure it routes text payloads to the correct endpoint. This dynamic lookup prevents routing errors when your operations team deploys a new pipeline version. The agent automatically discovers the active endpoints and adjusts its tool calls accordingly.

Setup guide

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

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Common questions about MonkeyLearn MCP in Google ADK

Run `pip install google-adk` and create an instance of `McpToolset` using the `StreamableHttpServerParameters` pointing to your Vinkius endpoint. Pass this toolset directly into the `tools` list of your `LlmAgent`.

Yes, you can pass a list of allowed tool names to your `McpToolset` constructor. This limits your agent to specific operations like `classify_text` while hiding administration tools.

The ADK relies on your local transport layer to handle HTTP backoff. If `classify_text` returns a rate limit error, the agent pauses before retrying the operation to avoid pipeline failures.

Execute `get_classifier_details` to retrieve the active tag list and model information. This provides your agent with the exact categories it should expect from classification runs.

Your customer support tickets are transmitted through an isolated, zero-trust connection. The Vinkius sandbox destroys the session immediately after the API call completes, leaving no trace of the ticket content on the host.

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