How to Use the HubSpot Service Hub MCP in OpenAI Agents SDK

Q: How do I install the HubSpot Service Hub MCP Server with OpenAI Agents SDK?

Run pip install openai-agents. You initialize MCPServerStreamableHttp with your endpoint URL and pass it to the agent constructor. Set cacheToolsList=True so it discovers the tools faster.

Q: Can OpenAI Agents SDK automatically escalate HubSpot Service Hub tickets?

Yes. Your agent checks the queue with hssearchtickets and evaluates the text. It then calls hsupdateticket to bump the priority to HIGH if the customer indicates an outage.

Q: How does the agent know which HubSpot Service Hub pipeline to use?

It calls hsticketpipelines first. This returns the exact stage labels and internal IDs. The agent uses those IDs when creating or moving tickets.

Connect the OpenAI Agents SDK to your support queue via this MCP Server. Let your production agents manage tickets and read feedback safely.

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Connect HubSpot Service Hub MCP to OpenAI Agents SDK

Create your Vinkius account to connect HubSpot Service Hub 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 HubSpot Service Hub with OpenAI Agents SDK

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Build support agents with OpenAI Agents SDK

`hs_create_ticket` lets your OpenAI agent open new support cases directly from user interactions. You pass the subject, priority, and pipeline stage. The agent handles the triage logic before execution, while the SDK guardrails ensure no malformed tickets hit your CRM. You route high-priority issues to specialized agents. If a customer sounds angry, the agent triggers `hs_update_ticket` to escalate the priority to HIGH. Full tracing in the OpenAI dashboard shows exactly why the handoff happened.

Monitor pipelines with this MCP Server

`hs_ticket_pipelines` pulls your exact support workflow stages right into the agent's context window. Instead of guessing stage IDs, the agent reads the actual internal IDs and display orders. It knows that a ticket in the 'Waiting on Us' stage requires immediate action. Once the agent maps the pipeline, it runs `hs_tickets_by_status` to measure queue depth. If the queue hits a threshold, your automated system alerts human managers. The agent never acts blindly.

Analyze customer satisfaction scores

`hs_list_feedback` retrieves raw survey submissions, including NPS, CSAT, and CES ratings. The agent reads the actual customer response content instead of just looking at historical averages. You tie this into your agentic workflow to catch bad interactions fast. When an agent spots a low rating, it instantly fires off `hs_search_tickets` to find the specific case and flag it for manual review.

Setup guide

Set up HubSpot Service Hub 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 HubSpot Service Hub tools at runtime.
3

Create your Agent

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

Run the agent

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

Run `pip install openai-agents`. You initialize `MCPServerStreamableHttp` with your endpoint URL and pass it to the agent constructor. Set `cacheToolsList=True` so it discovers the tools faster.

Yes. Your agent checks the queue with `hs_search_tickets` and evaluates the text. It then calls `hs_update_ticket` to bump the priority to HIGH if the customer indicates an outage.

It calls `hs_ticket_pipelines` first. This returns the exact stage labels and internal IDs. The agent uses those IDs when creating or moving tickets.

Every tool call logs directly to the OpenAI dashboard. You see the exact payload sent to `hs_create_ticket` and the JSON response that came back.

This MCP integration processes customer feedback text, ticket subjects, and internal CRM IDs. The V8 Isolate Sandbox destroys the environment after the agent finishes the request. Your connection requires a single endpoint token, and zero data rests on Vinkius infrastructure.

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