How to Use the Cleveland Museum of Art MCP in Pydantic AI

Validate Cleveland Museum of Art data in Pydantic AI with strict type enforcement and runtime error checking.

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Connect Cleveland Museum of Art MCP to Pydantic AI

Create your Vinkius account to connect Cleveland Museum of Art 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 Cleveland Museum of Art with Pydantic AI

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Claude

Perplexity

Type-safe art queries

Your agent executes `get_artwork` while Pydantic AI validates every field against your schemas. If the museum returns garbage, the agent stops immediately. This prevents silent failures when you fetch data. You'll know exactly what the structure looks like before your code touches it.

Validated search in Pydantic AI

Use `search_creators` to find artists and rely on Pydantic AI to enforce the data types. Your agent rejects malformed responses before they corrupt your logic. It handles the `search_artworks` output with the same rigor. You get clean, typed objects to work with every time.

Exhibition data in Pydantic AI

Fetch exhibition details via `get_exhibition` and ensure the output matches your expected model. This keeps your agent's knowledge base consistent. Your application code uses the `MCPToolset` to interact with the Cleveland Museum of Art server. It provides a stable bridge for your typed agents.

Setup guide

Set up Cleveland Museum of Art 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": {
        "cleveland-museum-of-art-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Cleveland Museum of Art transactions")
print(result.output)

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Common questions about Cleveland Museum of Art MCP in Pydantic AI

It uses your defined Pydantic models to verify the JSON output from the MCP server. If the data doesn't match your schema, the agent raises a validation error.

Yes, by adding the toolset to your agent instance. It handles the communication and ensures the results are typed correctly.

They are. The server only transmits public data, and you control the specific tool calls made during the agent execution.

It supports every tool exposed by the server. You can choose which ones to include in your `MCPToolset` definition.

The agent will fail and report the mismatch. This protects your system from processing bad data or hallucinatory fields.

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