How to Use the AssessTEAM MCP in OpenAI Agents SDK

Q: How do I link AssessTEAM with OpenAI Agents SDK?

You initialize the connection using the MCPServerStreamableHttp class. Pass your Vinkius endpoint into the constructor and add the server to your Agent's configuration.

Q: Can OpenAI Agents SDK pull performance reviews from AssessTEAM?

Yes. The agent uses listperformanceevaluations to retrieve review data for your team members. It processes the text, letting you summarize feedback during a chat session.

Track employee performance and project margins directly within your OpenAI Agents SDK production environment.

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Connect AssessTEAM MCP to OpenAI Agents SDK

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

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Manage team hours via OpenAI Agents SDK

Your agent handles time tracking by invoking `create_timesheet_entry` based on conversation context. It logs work hours without you needing to open the web dashboard. Running `list_timesheets` allows the agent to audit past entries for accuracy. This direct connection keeps your project logs updated while your agent manages the reporting flow.

Generate profitability data in OpenAI Agents SDK

Call `get_profitability_report` to pull real-time margin data into your agent's reasoning loop. You see exactly how project costs align with current billable hours. Use `list_projects` to define the scope of these reports. The agent parses the output to highlight which accounts currently drain your budget.

Audit team rosters for OpenAI Agents SDK

Automate personnel lookups using `list_employees` to identify staff availability for new tasks. It pulls names and roles into your agent's memory instantly. Pair this with `list_organizational_teams` to filter by department. You get a clean list of stakeholders before the agent assigns any follow-up work.

Setup guide

Set up AssessTEAM 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 AssessTEAM tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate AssessTEAM 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="AssessTEAM Agent",
            instructions="You have access to AssessTEAM tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AssessTEAM. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AssessTEAM MCP in OpenAI Agents SDK

You initialize the connection using the `MCPServerStreamableHttp` class. Pass your Vinkius endpoint into the constructor and add the server to your Agent's configuration.

Yes. The agent uses `list_performance_evaluations` to retrieve review data for your team members. It processes the text, letting you summarize feedback during a chat session.

Your data remains restricted to the authenticated session established by the MCP server. We handle the transport layer, ensuring your employee records are never exposed to unauthorized endpoints.

The server abstracts the API structure, so your agent continues calling the same tools. You don't need to rewrite your agent's logic when the backend updates.

No. The server acts as a pass-through for employee names and review scores. It does not cache your sensitive performance data on Vinkius infrastructure.

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