How to Use the Workast MCP in OpenAI Agents SDK

Q: Can I list all projects using Workast with OpenAI Agents SDK?

Yes. You can call listspaces to get a comprehensive list of every project space you're involved in. This helps the agent scope out exactly where it needs to start working.

Q: How do I update a task using Workast with OpenAI Agents SDK?

You simply tell the agent to use updatetask. It handles applying changes like new descriptions or due dates, ensuring your production pipeline stays accurate. The process is fully traceable in the dashboard.

Run complex Workast project workflows with the OpenAI Agents SDK.

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Connect Workast MCP to OpenAI Agents SDK

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

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Manage all your work spaces.

The agent can create a new environment using `create_space` or list every existing workspace via `list_spaces`. This keeps your project structure contained and easy to navigate. It handles space details through the `get_space` tool, giving you immediate context on any given project. You don't have to manually check multiple dashboards; just let your agent handle the setup.

Handle tasks from start to finish.

Need to change a deadline or assign ownership? Use `update_task` directly. If a task is obsolete, `delete_task` cleans it up immediately. The agent manages the entire lifecycle of any project item. It also gives you full visibility with `get_task`, providing all the details for a specific piece of work when you need to review progress.

List and manage team members.

Your AI client uses `list_users` to pull every team member's credentials. It also runs `get_team_info`, which gives an overview of the entire collaboration group. If you need to know what a specific person is working on, `get_user` fetches all their details instantly.

Setup guide

Set up Workast 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 Workast tools at runtime.
3

Create your Agent

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

Run the agent

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

Your agent discovers Workast's tools automatically; no setup code is needed. The unique advantage here is that the built-in guardrails validate every action before it runs, keeping your process safe and reliable.

Yes. You can call `list_spaces` to get a comprehensive list of every project space you're involved in. This helps the agent scope out exactly where it needs to start working.

It provides core collaboration data, including user profiles via `get_user`, team details, and all task records. This means your agent can build sophisticated workflows around people and projects.

You simply tell the agent to use `update_task`. It handles applying changes like new descriptions or due dates, ensuring your production pipeline stays accurate. The process is fully traceable in the dashboard.

Absolutely. The agent system has built-in guardrails that validate actions before execution and supports full tracing, so you always know exactly what happened inside the MCP Server.

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