How to Use the CORE (Open Access Research) MCP in Pydantic AI

Get type-safe research data for your Pydantic AI agents using this MCP server.

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Connect CORE (Open Access Research) MCP to Pydantic AI

Create your Vinkius account to connect CORE (Open Access Research) 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 CORE (Open Access Research) with Pydantic AI

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Type-check CORE (Open Access Research) results in Pydantic AI

Every response from the server is strictly validated against your Pydantic schemas. If a tool like `get_journal` returns malformed data, your agent catches it immediately. This stops the agent from hallucinating fields or crashing on unexpected API changes. You maintain full control over the data structure entering your application logic.

Handle research errors in Pydantic AI

The server's tools provide consistent outputs that integrate with your runtime validation rules. You define what a valid article looks like, and the MCP server satisfies that contract. This ensures your agent works with clean data, not just whatever the API sends. It prevents silent failures by forcing the agent to deal with validation errors explicitly.

Connect CORE (Open Access Research) to any Pydantic AI model

Because the toolset is model-agnostic, you can swap between local models and large cloud models without changing your tool definitions. You keep your research logic consistent across your entire agent fleet. This MCP server exposes all 10 tools with clearly defined types. You spend less time debugging API responses and more time building your application.

Setup guide

Set up CORE (Open Access Research) 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": {
        "core-open-access-research-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent CORE (Open Access Research) transactions")
print(result.output)

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Common questions about CORE (Open Access Research) MCP in Pydantic AI

You get runtime validation for every research query. If the API returns something unexpected, your agent fails fast instead of proceeding with bad data. It provides the type safety your production code demands.

It supports both Streamable HTTP and SSE. You can choose the transport that matches your existing architecture. The integration is unified and follows current framework standards.

Yes, you define your Pydantic models in your agent code. When the tool returns data, the framework validates it against those models. It is the best way to handle external API output.

Install the slim MCP package and use the MCPToolset class. Pass your server URL and you are ready to go. It is designed for simple, reliable integration.

The server only processes open-access bibliographic records. It has no access to your Pydantic models or your private business logic. We ensure that only public research information is ever handled by the server.

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