How to Use the Vercel MCP in OpenAI Agents SDK
Control Vercel deployments and infrastructure with your OpenAI Agents SDK.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Vercel MCP to OpenAI Agents SDK
Create your Vinkius account to connect Vercel 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.
Manage all project lifecycles
The `list_projects` tool lets you see every single Vercel project in the account. You can then use `get_project_details` to pull specific configuration data for any of them. You also have `create_project`, which handles making a new Vercel site based on a name and framework slug.
Inspect deployment status
`list_deployments` shows you all recent CI/CD builds for a specific project. Need to know what happened with the latest run? Use `get_deployment_details` to pull that full execution history. It's also smart to check out `list_project_aliases`, which maps out exactly how your project is routed via subdomains.
Trigger and control builds
If you need a fresh build, just call `trigger_github_deployment` with the required project name and Git ref. This immediately starts a new Vercel compile pipeline. When you're done testing or if something goes wrong, `cancel_active_build` aborts any ongoing compilation process.
Set up Vercel 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 Vercel tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives Vercel tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate Vercel 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="Vercel Agent",
instructions="You have access to Vercel 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 Vercel. 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 Vercel MCP in OpenAI Agents SDK
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