How to Use the MonkeyLearn MCP in Google ADK

Q: How does the Google ADK agent know which MonkeyLearn model version to use?

Your agent can call listmodelversions to see every deployment of a specific classifier. It can then select the active production version before sending text to classifytext.

Connect your Google ADK agents to MonkeyLearn via MCP to analyze enterprise support data directly from BigQuery.

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Connect MonkeyLearn MCP to Google ADK

Create your Vinkius account to connect MonkeyLearn 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 MonkeyLearn with Google ADK

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Analyze high-volume support data from BigQuery

Enterprise data lives in databases, not text files. Your Gemini agent can pull thousands of customer rows and pass them to `run_workflow` to classify sentiment and extract key details in parallel. Since Gemini handles huge context windows, you can feed entire conversation histories into `classify_text`. The agent uses `list_nlp_workflows` to find the exact pipeline needed for your specific department's data.

Manage active classifiers inside your Google ADK agent

Keep your agents aligned with your production machine learning models. The agent uses `list_classifiers` to discover what models are active, then grabs specific metadata using `get_classifier_details` to ensure it targets the right version. This prevents your Gemini agent from hallucinating tags or using outdated categories. By calling `list_classifier_tags`, the agent always knows the exact taxonomy it needs to apply to incoming customer queries.

Deploy structured entity extraction at scale

Pulling features out of unstructured documents is simple with this MCP Server. Your agent calls `extract_text_entities` to instantly isolate names, locations, or custom business keys from emails. To keep things organized, the agent can query `list_extractors` to choose the correct extraction model. It then checks `list_extractor_tags` to confirm the exact fields it should expect in the response.

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

Your agent can call `list_model_versions` to see every deployment of a specific classifier. It can then select the active production version before sending text to `classify_text`.

Yes, you can pass a specific list of tool names when initializing the server parameters. This restricts the agent so it can only run `classify_text` or `extract_text_entities` without accessing model configuration tools.

Absolutely. Gemini can ingest massive logs, identify the critical sections, and then use the MCP Server to run targeted extraction via `extract_text_entities` on those specific chunks.

Run the server on Vinkius and use the HTTP transport inside your Python code. The ADK handles the network calls, letting your agent run on Vertex AI while communicating with the API.

Yes, all data transmission is encrypted in transit using TLS 1.3. Vinkius operates a zero-trust architecture, meaning your API tokens and raw text payloads are processed in memory and never written to persistent storage.

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