How to Use the Fountain MCP in OpenAI Agents SDK

Q: How do I configure the Fountain MCP Server with the OpenAI Agents SDK?

Use pip install openai-agents. Initialize the connection with MCPServerStreamableHttp pointing to your Vinkius endpoint URL. Pass the server instance to your Agent constructor.

Q: How do I cache Fountain tool definitions in my agent?

Set cacheToolsList=True when configuring the server. This prevents the agent from re-fetching the schema on every run, saving time and tokens.

Connect the Fountain MCP Server to your OpenAI Agents SDK system to automate high-volume hiring pipelines safely.

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Connect Fountain MCP to OpenAI Agents SDK

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

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Automate Applicant Tracking

The `list_applicants` and `get_applicant` tools pull exact candidate profiles directly into your agent's context. Your system scans thousands of applications without human intervention. The agent checks standard criteria and filters out unmatched candidates immediately. Built-in guardrails in the SDK ensure your agent never misinterprets the data. You set the rules, and the system follows them. If a candidate passes the initial screen, the agent flags them for the next stage.

OpenAI Agents SDK Funnels

Managing complex hiring pipelines requires the `list_funnels` and `list_funnel_stages` operations. Your OpenAI Agents SDK setup reads the exact structure of your recruitment process. It knows exactly where each candidate sits in the pipeline. Handoffs between specialized agents happen naturally here. One agent handles top-of-funnel screening, while another takes over when candidates reach the interview stage. This MCP Server integration lets you track the entire flow through the OpenAI dashboard.

Audit Interview Notes

Reading human feedback requires the `list_applicant_notes` and `list_interview_sessions` tools. Your agent fetches scheduled times and interviewer comments to determine next steps. It cross-references available slots with candidate status. Tracing these actions is critical for compliance. Because the SDK logs every tool call, you always know why an agent rejected a candidate. The system provides a clear audit trail for every hiring decision.

Setup guide

Set up Fountain 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 Fountain tools at runtime.
3

Create your Agent

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

Run the agent

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

Use `pip install openai-agents`. Initialize the connection with `MCPServerStreamableHttp` pointing to your Vinkius endpoint URL. Pass the server instance to your Agent constructor.

Yes. You can build specialized agents for different stages. One agent handles sourcing, then hands off the candidate to a scheduling agent.

The SDK handles retries and backoffs automatically. If the server returns a rate limit error, your agent pauses before trying again.

Set `cacheToolsList=True` when configuring the server. This prevents the agent from re-fetching the schema on every run, saving time and tokens.

We protect applicant PII and interview notes strictly. The V8 Isolate Sandbox executes code in a zero-trust, ephemeral environment. Your data never persists after the session closes.

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