How to Use the Deskpro MCP in Pydantic AI

Build type-safe support agents with Deskpro and Pydantic AI.

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Connect Deskpro MCP to Pydantic AI

Create your Vinkius account to connect Deskpro 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 Deskpro with Pydantic AI

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Type-safe ticket management in Pydantic AI

Every response from `get_ticket_details` is validated against your Pydantic models. If the API returns a malformed field, your agent stops immediately instead of propagating bad data. This ensures your `create_new_helpdesk_ticket` calls always contain the correct fields. Your code remains stable even if the API schema changes slightly.

Validated user lookups for Pydantic AI

The agent fetches user profiles via `get_user_profile` and maps them to your local data structures. It strictly enforces types for user IDs and contact info. When listing users with `list_helpdesk_users`, the output is parsed and verified. You get clean objects ready for use in your agent's decision logic.

Reliable knowledge retrieval for Deskpro

Your agent calls `list_kb_articles` and verifies the returned list against your expected output format. It guarantees that every article reference is valid before presenting it to a user. It handles `get_article_content` with strict schema checks. Your agent only acts on data that matches your defined structures.

Setup guide

Set up Deskpro 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": {
        "deskpro-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Deskpro transactions")
print(result.output)

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Common questions about Deskpro MCP in Pydantic AI

The MCP tools are wrapped in Pydantic models at runtime. If the server returns something unexpected, your agent throws a validation error instead of crashing later.

You can. The strict type validation makes this a solid choice for production agents where data integrity is your top priority.

You instantiate the MCPToolset with the server URL and pass it to your agent. The framework handles the rest, ensuring all tool calls are typed.

It does. You can access every function from ticket creation to webhook listing while keeping your entire pipeline type-safe.

We isolate your data transport. Since your agent validates every incoming object, you have an extra layer of defense against unexpected data injection.

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