How to Use the Keen MCP in OpenAI Agents SDK

Connect Keen to your OpenAI Agents SDK production pipeline for instant event analysis and structured data tracking.

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Connect Keen MCP to OpenAI Agents SDK

Create your Vinkius account to connect Keen 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 Keen with OpenAI Agents SDK

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Real-time event recording with OpenAI Agents SDK

Your agent can pipe telemetry directly into Keen using `record_event`. This lets your system log state changes or user interactions without manual database writes. By hooking this into your agent lifecycle, you get a persistent audit trail of every action. It eliminates the need for custom logging infrastructure inside your production environment.

Run analytics directly from OpenAI Agents SDK

Use `count_events` or `sum_property` to pull metrics during a conversation. Your agent can digest these figures to make informed decisions based on real-time data trends. This bypasses the usual lag of pulling data through a backend API. The agent queries Keen directly and receives the processed numbers as structured context.

Explore project data with OpenAI Agents SDK

The agent can map out your entire analytics structure using `list_collections` and `get_project_details`. It understands what events are available before it attempts a query. This prevents runtime errors caused by mismatched schema names. Your agent stays synchronized with the current state of your Keen project configuration.

Setup guide

Set up Keen 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 Keen tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Keen 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="Keen Agent",
            instructions="You have access to Keen 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 Keen MCP in OpenAI Agents SDK

You connect them using the MCPServerStreamableHttp class. Once initialized, the SDK automatically pulls in the Keen tools for your agent to use.

Yes. Since the MCP server runs externally, your agent just receives the final results. This keeps your Python environment clean and focused on task execution.

Keen handles your event data through encrypted channels. Only your authenticated agent can trigger queries or record events, keeping your telemetry isolated.

The MCP server returns a standard error code that the agent interprets. You can catch these exceptions in your agent logic to retry or log the failure.

Absolutely. You can define specialized agents that only have access to specific Keen tools, limiting what they can do with your event data.

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