How to Use the Feathery MCP in OpenAI Agents SDK

Manage Feathery forms and user sessions directly inside your OpenAI Agents SDK production system.

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Connect Feathery MCP to OpenAI Agents SDK

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

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Full Feathery control for OpenAI Agents SDK

Your agents can query form states without leaving the execution loop. Use `get_form_session` to pull live user data or `get_form_details` to inspect structure. This MCP Server provides the hooks you need for building reliable data pipelines. Your code handles the logic while the agent manages the Feathery API calls.

Real-time log monitoring for your agents

Stop guessing why a webhook failed. Your agent can call `list_connector_logs` to surface specific error details instantly. It’s a straightforward way to keep your operations running. If something breaks, your agent identifies the root cause and reports it back to your dashboard.

Automated user and workflow management

You can now list every active user or workflow using `list_users` and `list_workflows`. It’s useful for syncing internal records with your Feathery account. Automate the boring parts of your pipeline. Your agent handles the heavy lifting of data retrieval so you can focus on the business logic.

Setup guide

Set up Feathery 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 Feathery tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Feathery 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="Feathery Agent",
            instructions="You have access to Feathery tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 Feathery. 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 Feathery MCP in OpenAI Agents SDK

Use the MCPServerStreamableHttp class to point your agent at the Feathery endpoint. Once you pass the server to the Agent constructor, your agent auto-discovers every available tool.

Yes. This setup is built for deployed products rather than prototypes. It relies on direct API communication between your agent and the Feathery infrastructure.

Absolutely. You can wrap these tool calls in your existing validation logic. The agent checks action requests before execution to ensure data integrity.

Set cacheToolsList=True in your server configuration. This keeps performance high by preventing redundant discovery calls.

Feathery only exposes form submissions and workflow metadata through explicit tool calls. Your agent never touches raw database files, only the specific session data you request.

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