How to Use the Vercel MCP in AutoGen

Q: How does AutoGen use the Vercel MCP Server to list projects?

A dedicated agent calls listprojects as its initial action. This output becomes the shared context that all other agents debate against, forcing a structured discussion around your existing infrastructure.

Q: Can AutoGen use the Vercel MCP Server to check deployment status?

Yes. Agents can call getdeploymentdetails and pass this output into the deliberation process. The agents then debate whether the current status is acceptable or if manual intervention is needed.

Q: If I'm building an app with Vercel and AutoGen, how do I check domain aliases?

Agents can use listprojectaliases to gather all routing mappings. The consensus task might involve debating which alias needs updating or why the current list is incomplete.

Facilitate consensus decisions on Vercel deployments using AutoGen and the MCP Server.

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Setup Vercel with AutoGen

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Automated Deployment Review with AutoGen

Start by listing all projects using `list_projects`. Different agents (e.g., a Security Agent, a Performance Agent) can each review this output and debate the risks or requirements of each project. This forces consensus-driven decision making before any action is taken.

Triggering Builds via AutoGen

One agent might propose that a new build is needed. It calls `trigger_github_deployment` with the project name and Git ref. The other agents then debate if this manual trigger is necessary or if a different deployment method should be used. This simulates a robust, multi-step review process.

Managing Vercel Infrastructure

Agents can use `create_project` to provision new services after reaching consensus on the required framework and name. Conversely, if a project is deemed obsolete, an agent can call `delete_project`, with all parties acknowledging the irreversible nature of the action. This builds systems where critical decisions require multiple viewpoints.

Setup guide

Set up Vercel MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Vercel tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Vercel_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vercel data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Vercel_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vercel data")
    print(result.messages[-1].content)

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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 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.

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Common questions about Vercel MCP in AutoGen

A dedicated agent calls `list_projects` as its initial action. This output becomes the shared context that all other agents debate against, forcing a structured discussion around your existing infrastructure.

Yes. Agents can call `get_deployment_details` and pass this output into the deliberation process. The agents then debate whether the current status is acceptable or if manual intervention is needed.

Agents can use `list_project_aliases` to gather all routing mappings. The consensus task might involve debating which alias needs updating or why the current list is incomplete.

The server exposes Project configuration and deployment metadata. AutoGen's agents process this structured output, using it to challenge assumptions and refine a final decision.

The server touches Project configuration and deployment metadata. The raw API results are passed through multiple agent turns, debated against policy rules, until a final, agreed-upon action is determined.

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