How to Use the Unleash (Feature Toggles) MCP in OpenAI Agents SDK

Q: How do I check flag status with Unleash (Feature Toggles) on OpenAI Agents SDK?

Use getclientfeatures to pull all active flags for your server-side evaluation. The tool returns the current strategy and state, telling your agent if it needs to run that code path or skip it entirely.

Q: Can I list projects using Unleash (Feature Toggles) with OpenAI Agents SDK?

Yes, call listprojects to get every configured project ID. Once you have a list of IDs, your agent can use listprojectfeatures to check the specific flags relevant to that single project.

Q: What data types are touched when I report metrics with Unleash (Feature Toggles) on OpenAI Agents SDK?

The metrics tools, specifically reportclientmetrics and reportfrontendmetrics, handle flag usage metadata. They track which features were accessed and by what client context.

Q: Does the MCP Server help me list all environments for Unleash (Feature Toggles) using OpenAI Agents SDK?

The listenvironments tool fetches every environment configured in Unleash. This is critical because a feature flag's status changes depending on whether you're running in staging, production, or development.

Q: What if I need to check flags for users? How does Unleash (Feature Toggles) work with OpenAI Agents SDK?

You can use listusers to retrieve all configured user accounts. Then, you pass the necessary context into feature tools like getclientfeatures so your agent evaluates the flag correctly for that specific user.

Build production agents with Unleash (Feature Toggles) using OpenAI Agents SDK.

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Connect Unleash (Feature Toggles) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Unleash (Feature Toggles) 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 Unleash (Feature Toggles) with OpenAI Agents SDK

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Evaluate real-time feature flags.

The `get_client_features` tool fetches all active feature flags and strategies for your server. Your agent uses this data to decide if a feature is live, allowing you to test code paths before general release. If you need context—like knowing the user ID or specific properties—use `get_frontend_features`. This helps the MCP Server pass enough detail so your agent makes accurate decisions about flag evaluation.

Manage project visibility and scope.

Need to know what features are available? Run `list_projects` to retrieve every configured Unleash project. You can then narrow down your focus using `list_project_features`, which checks flags for a specific ID. This ability lets your agent scope its work immediately. It prevents the agent from wasting time checking irrelevant feature sets by giving it defined boundaries first.

Monitor flag usage and performance metrics.

Keep tabs on how people use your flags with `report_client_metrics`. This tool allows your backend SDK to send back usage data, letting you see exactly which toggles are being hit. You can also report frontend activity using `report_frontend_metrics`. Monitoring these metrics keeps the whole MCP Server visible and gives you actionable insights into user behavior.

Setup guide

Set up Unleash (Feature Toggles) 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 Unleash (Feature Toggles) tools at runtime.
3

Create your Agent

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

Run the agent

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

Use `get_client_features` to pull all active flags for your server-side evaluation. The tool returns the current strategy and state, telling your agent if it needs to run that code path or skip it entirely.

Yes, call `list_projects` to get every configured project ID. Once you have a list of IDs, your agent can use `list_project_features` to check the specific flags relevant to that single project.

The metrics tools, specifically `report_client_metrics` and `report_frontend_metrics`, handle flag usage metadata. They track which features were accessed and by what client context.

The `list_environments` tool fetches every environment configured in Unleash. This is critical because a feature flag's status changes depending on whether you're running in staging, production, or development.

You can use `list_users` to retrieve all configured user accounts. Then, you pass the necessary context into feature tools like `get_client_features` so your agent evaluates the flag correctly for that specific user.

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