How to Use the Universities List MCP in Pydantic AI

Q: How do I use Universities List with Pydantic AI?

You pass the MCP toolset to the Agent, defining it via MCPToolset. The framework then handles calling searchuniversities and validating the resulting data structure.

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Connect Universities List MCP to Pydantic AI

Create your Vinkius account to connect Universities List 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 Universities List with Pydantic AI

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Search by Name or Country

When you need to find universities, the agent executes `search_universities`. The tool takes your input—be it a name or a country—and returns results. It's always type-safe; you know exactly what structure of data you’re getting back.

Validation Against Pydantic Models

This is the main selling point: if the API response deviates from your defined schema, the agent fails loudly. You don't get corrupted or unexpected data fields. It eliminates silent failures and makes building production-grade agents much safer.

Model Agnostic Framework

The framework works regardless of which LLM you use—OpenAI, Anthropic, Gemini, local models. You just define the toolset once. This gives you flexibility because your core logic is decoupled from any single model provider.

Setup guide

Set up Universities List 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": {
        "universities-list-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Universities List MCP in Pydantic AI

You pass the MCP toolset to the Agent, defining it via `MCPToolset`. The framework then handles calling `search_universities` and validating the resulting data structure.

The server interacts with university names and country identifiers. These two string types are what you'll be working with, and Pydantic ensures they arrive in the expected format.

Yes. Because your toolset is defined by its schema and not by the underlying model, you can switch from OpenAI to Anthropic without changing how `search_universities` is called.

The agent immediately throws a validation error. This prevents silent corruption and forces you to fix the issue, which is exactly what you want in production code.

It's great because it guarantees correctness at every step. When accuracy is more important than speed, the runtime validation provided by Pydantic is unmatched.

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