How to Use the Insightful MCP in OpenAI Agents SDK

Q: How do I configure the Insightful toolset in the OpenAI Agents SDK?

Install the SDK first. Use MCPServerStreamableHttp with your Vinkius MCP endpoint URL and pass it to your Agent constructor. Setting cacheToolsList=True keeps your tool discovery fast and efficient.

Q: Does the OpenAI Agents SDK handle rate limits when querying Insightful activity logs?

High-frequency queries to listactivity can trigger rate limits from the API. You should implement backoff logic in your Python code to handle these limits during peak hours.

Put employee productivity logs and time tracking directly into your OpenAI Agents SDK production runs.

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Connect Insightful MCP to OpenAI Agents SDK

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

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Audit operations with OpenAI Agents SDK and Insightful.

The `get_attendance` tool pulls aggregate time-tracking data so your agent can verify shift coverage without manual spreadsheet reviews. Your agent uses `get_employee` alongside this to match raw hours with specific staff profiles in your organization. This integration runs inside your agent's decision loop to flag missing shifts before payroll runs. The OpenAI dashboard tracks every single tool call, giving you a complete audit trail of how your agent processes worker hours.

Map active work logs with this MCP Server.

This MCP Server provides `list_activity` to feed raw, active work events directly to your agent for real-time analysis. The agent matches these logs against `get_productivity` metrics to see if team output aligns with scheduled hours. Guardrails in the OpenAI Agents SDK validate these analytical steps before your agent writes any reports. You get clean, verified metrics on team output without risking hallucinated productivity scores.

Route tasks across departments automatically.

The `list_tasks` tool exposes active assignments so your agent can check project statuses instantly. By combining this with `list_teams` and `list_projects`, your agent maps exactly who is working on what across your entire organization. Specialized agents in your SDK hand off tasks to one another based on these live structures. If an agent notices a bottleneck in a specific location from `list_locations`, it routes the ticket to the correct regional lead.

Setup guide

Set up Insightful 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 Insightful tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the SDK first. Use `MCPServerStreamableHttp` with your Vinkius MCP endpoint URL and pass it to your Agent constructor. Setting `cacheToolsList=True` keeps your tool discovery fast and efficient.

You control this at the agent definition level. Define specific system prompts and guardrails in your Python code to restrict access to tools like `list_webhooks`. This keeps your agent focused solely on productivity data.

High-frequency queries to `list_activity` can trigger rate limits from the API. You should implement backoff logic in your Python code to handle these limits during peak hours.

The SDK automatically registers all 10 tools from the MCP server at runtime. Your agent reads the JSON schemas for tools like `list_employees` and immediately understands how to format its queries.

Your attendance records and productivity metrics remain strictly within your secure Vinkius environment. The OpenAI Agents SDK executes calls through an ephemeral MCP sandbox, meaning your raw employee data is never stored on external servers.

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