How to Use the MonkeyLearn MCP in OpenAI Agents SDK

Q: How do I handle rate limits when using MonkeyLearn with the OpenAI Agents SDK?

Use the getapistatus tool to monitor your plan limits before kicking off massive batch jobs. If you hit a limit, the SDK's built-in guardrails catch the error, allowing your agent to pause or back off gracefully.

Q: Can my OpenAI Agents SDK dynamically switch between different MonkeyLearn models?

Yes, the agent can call listclassifiers or listextractors to see what is available. From there, it selects the best model ID and queries getclassifierdetails to verify the version before running the text through it.

Build production-ready OpenAI Agents that automatically classify support tickets using this MonkeyLearn MCP Server.

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Connect MonkeyLearn MCP to OpenAI Agents SDK

Create your Vinkius account to connect MonkeyLearn to OpenAI Agents SDK 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 OpenAI Agents SDK

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Run automated text classification pipelines

Stop manually routing incoming user feedback. Your agent can immediately analyze and categorize incoming messages by calling `classify_text` to detect sentiment or topic. It pulls the exact tags you set up in your dashboard using `list_classifier_tags` to keep routing rules completely accurate. This setup runs directly within your agent workflow. By query-checking your model status with `get_classifier_details`, the agent ensures it only sends payloads to active, trained models.

Extract deep metadata inside your OpenAI Agents SDK

Pulling structured data out of messy text shouldn't require complex regex. This MCP server lets your OpenAI agents call `extract_text_entities` to grab product names, addresses, or serial numbers from raw support tickets in milliseconds. You can inspect your active extraction models using `list_extractors` to verify the agent uses the right parser. If you change your entities in the dashboard, the agent automatically adapts by fetching the updated schema with `list_extractor_tags`.

Execute complex NLP workflows with built-in tracing

Combine multiple analysis steps into a single API call instead of chaining multiple model runs. Your agent uses `run_workflow` to process text through multi-step pipelines that handle both classification and extraction at once. Because you are using this MCP Server with the OpenAI Agents SDK, every step of the pipeline is tracked. You can monitor exactly which workflow ran and watch the raw output stream directly into your telemetry dashboard.

Setup guide

Set up MonkeyLearn MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all MonkeyLearn tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives MonkeyLearn tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate MonkeyLearn tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="MonkeyLearn Agent",
            instructions="You have access to MonkeyLearn tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about MonkeyLearn MCP in OpenAI Agents SDK

Use the `get_api_status` tool to monitor your plan limits before kicking off massive batch jobs. If you hit a limit, the SDK's built-in guardrails catch the error, allowing your agent to pause or back off gracefully.

Yes, the agent can call `list_classifiers` or `list_extractors` to see what is available. From there, it selects the best model ID and queries `get_classifier_details` to verify the version before running the text through it.

You do not need to write any parsing code. The MCP Server outputs clean JSON that your agent inspects directly, allowing it to hand off the categorized data to other specialized agents instantly.

Install the SDK, then initialize the HTTP transport pointing to your Vinkius endpoint. Pass the server instance directly to your agent constructor, and the tools will auto-discover on startup.

Your text data goes directly to the MonkeyLearn API for processing and is never stored on Vinkius. The connection runs inside an isolated, ephemeral V8 sandbox that destroys all session data the moment the API call finishes.

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