How to Use the HubSpot Service Hub MCP in Pydantic AI

Q: How do I connect Pydantic AI to the HubSpot Service Hub MCP Server?

Install pydantic-ai-slimmcp. Initialize MCPToolset with your HTTP endpoint URL. Pass it in the toolsets array when you create your Agent.

Q: Can I use the older MCPServerHTTP class for this?

No. That class is deprecated. You must use the unified MCPToolset approach to handle Streamable HTTP and SSE transports correctly.

Bring strict type safety to your HubSpot Service Hub data with Pydantic AI and this MCP Server. No hallucinated ticket IDs.

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Connect HubSpot Service Hub MCP to Pydantic AI

Create your Vinkius account to connect HubSpot Service Hub to Pydantic AI 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 Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Create and update tickets with strict validation

`hs_create_ticket` pushes new support cases into your CRM using strongly typed parameters. You pass a subject, description, and an explicit priority like HIGH or LOW. Pydantic AI validates the inputs before the request even leaves your server. When a case progresses, the agent calls `hs_update_ticket`. If the LLM tries to invent a non-existent pipeline stage, the framework throws a validation error immediately. You get loud failures instead of silent data corruption.

Query exact pipeline structures

`hs_ticket_pipelines` fetches the hard truth about your CRM setup. It returns exact stage labels and internal IDs. Your agent uses this data to build a valid list of allowed states for future operations. Armed with those verified IDs, the agent runs `hs_tickets_by_status`. It pulls the exact queue depth for specific stages. Because Pydantic AI checks the response schema, you know the ticket counts and priorities match your expected data models.

Parse feedback with this MCP Server

`hs_list_feedback` pulls survey submissions, ratings, and raw text. The agent does not guess what the customer meant. It reads the exact CSAT or NPS score and the associated survey type. You write logic that only triggers when a score drops below a certain threshold. If a CES score is terrible, the agent executes `hs_search_tickets` to find the related case and investigate the history.

Setup guide

Set up HubSpot Service Hub MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "hubspot-service-hub-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to HubSpot Service Hub tools.",
)

result = await agent.run("List recent HubSpot Service Hub transactions")
print(result.output)

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Common questions about HubSpot Service Hub MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize `MCPToolset` with your HTTP endpoint URL. Pass it in the `toolsets` array when you create your Agent.

The agent fails loudly. Pydantic AI validates the exact schema required by `hs_update_ticket`. If the types do not match or the ID format is wrong, the runtime catches it before executing.

Pydantic AI is model-agnostic. You connect this MCP Server to OpenAI, Anthropic, Gemini, or even a local model. The strict type validation works exactly the same across all of them.

No. That class is deprecated. You must use the unified `MCPToolset` approach to handle Streamable HTTP and SSE transports correctly.

The tools access raw ticket subjects, priority flags, and customer survey text. We rely on a zero-trust architecture. The V8 Isolate spins up just long enough to return your data, then vanishes. You hold the single endpoint token, keeping your CRM credentials completely isolated.

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