How to Use the Front MCP in OpenAI Agents SDK

Q: What happens if the Front API is down when using the OpenAI Agents SDK?

Your agent will get a clear error. The server includes a getapistatus tool you can call, and any failed API request to tools like listteamcontacts will return a structured error instead of crashing the agent.

Manage Front conversations directly from your OpenAI Agents. Built-in guardrails mean your agent won't go rogue replying to customers.

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Connect Front MCP to OpenAI Agents SDK

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

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Claude

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Triage Inboxes Without Leaving Code

This MCP Server connects your OpenAI Agent directly to your team's shared inboxes. You can pull conversation lists with `list_conversations` and check on specific threads using `get_conversation_details`. It's a direct line of sight into your customer comms. Your agent can then decide what to do next. It can read the full `get_message_content`, find out who you're talking to with `get_contact_info`, and then hand off to another specialized agent for the actual reply.

Act on Conversations with Guardrails

The real work happens when your agent takes action. You can send replies using `reply_to_conversation` or simply archive a thread with `update_conversation_status`. The Agents SDK lets you define validation rules before any of these actions execute. This means you get full control. Your agent can't accidentally send a bad message because your guardrails will catch it. All actions are traced in the OpenAI dashboard, so you have a complete audit log of what your agent did and why.

Your OpenAI Agent's Front Toolkit

Connecting this to the OpenAI Agents SDK is straightforward. The tools are auto-discovered, so you don't need to manually define schemas for `list_shared_inboxes` or `search_conversations_by_query`. Just point the agent to the server endpoint. This lets you build agents that can answer questions like 'are there any new high-priority tickets?' or 'find the conversation with user_123 about their billing.' The agent figures out which tools to call, and you get the answer.

Setup guide

Set up Front 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 Front tools at runtime.
3

Create your Agent

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

Run the agent

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

It's automatic. When you pass the MCP Server to the Agent constructor, it queries the endpoint and registers all 12 tools like `list_conversations` and `reply_to_conversation` without any extra code.

Yes, that's a primary use case. Your agent can read incoming messages with `get_message_content`, use its own logic to decide on a response, and then send it with `reply_to_conversation`.

Your agent will get a clear error. The server includes a `get_api_status` tool you can call, and any failed API request to tools like `list_team_contacts` will return a structured error instead of crashing the agent.

It's faster. This server handles the authentication, error mapping, and tool definitions for you. You just focus on your agent's logic instead of writing boilerplate API connection code.

Your agent will handle sensitive customer data, including message content from `get_message_content` and contact info from `get_contact_info`. The connection is over HTTPS, and Vinkius runs each server in an ephemeral, sandboxed environment. Your Front API token is the key, so keep it secure.

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