How to Use the CERN Open Data MCP in Pydantic AI

Q: Can I search for B physics datasets from CERN Open Data using Pydantic AI?

Certainly. Use the searchbycategory tool and specify the B physics category to get a filtered list of relevant records.

Q: Is it easy to check the status of CERN Open Data in Pydantic AI?

You just call checkcernopendatastatus as part of your agent's startup sequence. It confirms the integration is ready for use.

Build type-safe physics data agents using Pydantic AI and the CERN Open Data MCP Server.

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Connect CERN Open Data MCP to Pydantic AI

Create your Vinkius account to connect CERN Open Data 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 CERN Open Data with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Validate record metadata with Pydantic AI

Every tool response, such as those from `get_record`, is validated against your schema. If the API returns unexpected fields, your agent catches the mismatch immediately. This keeps your agent stable and prevents runtime errors. You get a reliable interface for interacting with complex physics metadata without worrying about silent data corruption.

Explore physics documentation and guides

Your agent can query `search_documentation` to understand data processing workflows. This ensures that the datasets you retrieve are handled according to the experiment's specifications. Use this to build agents that don't just find data but also understand how to use it. It is the best way to ensure your analysis remains scientifically accurate.

Manage experiment-specific searches

Use `search_by_experiment` to isolate your work to specific collaborations like LHCb or ALICE. This tool provides a clean starting point for focused research. By combining this with `list_experiments`, your agent maintains a clear view of where its data is coming from. It simplifies the process of narrowing down thousands of datasets to the ones that matter.

Setup guide

Set up CERN Open Data 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": {
        "cern-open-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent CERN Open Data transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CERN Open Data. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about CERN Open Data MCP in Pydantic AI

The framework matches the JSON output from the MCP server against your Pydantic models. Any deviation triggers an immediate validation error.

Certainly. Use the `search_by_category` tool and specify the B physics category to get a filtered list of relevant records.

It does. You define the server connection, and the toolset maps the server's capabilities to your typed agent functions.

You just call `check_cern_opendata_status` as part of your agent's startup sequence. It confirms the integration is ready for use.

Security is maintained by limiting the scope to public scientific datasets. No PII or private credentials are ever exchanged, ensuring your agent environment remains secure and compliant.

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