How to Use the Unanet MCP in OpenAI Agents SDK
Build production agents that read Unanet data using OpenAI Agents SDK.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Unanet MCP to OpenAI Agents SDK
Create your Vinkius account to connect Unanet 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.
Access Core Financial Records
Your agent can pull basic details on projects in `projects`. You’ll see project names and associated identifiers, giving your workflow a solid starting point. It also handles financials. Need to check employee expenses? Just calling the `expenses` tool gives you access to report listings for any user.
Check Employee Time & Personnel Data
When you need people data, the agent uses `users` to list employees in Unanet. This is how your system stays current on who's working and what their roles are. The tool also runs `timesheets`, listing specific timesheet records for a given user. You can track work hours directly through this MCP Server.
Manage Project Workloads
Use the `projects` tool to list all available projects in Unanet. This lets your agent know what scope of work is active before it starts processing anything. Need to tie employee effort to a project? The system connects through listing both `timesheets` and `users`, giving you full visibility into resource allocation.
Set up Unanet MCP in OpenAI Agents SDK
Prerequisites
- Python 3.10+ installed
-
openai-agentspackage (pip install openai-agents) - Active Vinkius subscription with a valid endpoint token
- 1
Install the SDK
Run
pip install openai-agentsto install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed. - 2
Connect via SSE transport
Use
MCPServerSsewith your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. The SDK auto-discovers all Unanet tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives Unanet tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate Unanet tools and returns structured results. Copy the full example on the right to get started.
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="Unanet Agent",
instructions="You have access to Unanet tools.",
mcp_servers=[server],
)
result = await Runner.run(agent, "List recent transactions")
print(result.final_output)
asyncio.run(main())Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Unanet. 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.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Unanet MCP in OpenAI Agents SDK
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