How to Use the ClinicalTrials.gov MCP in Pydantic AI

Validate ClinicalTrials.gov data with Pydantic AI type safety.

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Connect ClinicalTrials.gov MCP to Pydantic AI

Create your Vinkius account to connect ClinicalTrials.gov 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 ClinicalTrials.gov with Pydantic AI

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Type-safe trial searching with Pydantic AI

Use `find_recruiting_trials` to get enrollment data that Pydantic AI validates against your models. If the registry returns bad data, your agent catches it immediately. This prevents silent failures in your research pipeline. You define the expected structure, and the framework ensures the data matches.

Fetch trial details in Pydantic AI

The `get_trial_details` tool pulls specific records into your type-safe environment. Pydantic AI parses the response and forces a validation error if anything is missing. You gain confidence in your agent's data processing. It handles the registry response as a structured object, not just a raw string.

Search the registry using Pydantic AI

Trigger `search_clinical_trials` to query the database with strict schema enforcement. You get predictable results for every keyword or condition search. Your agent relies on these validated objects to make decisions. It eliminates the risk of hallucinated fields or unexpected API changes.

Setup guide

Set up ClinicalTrials.gov 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": {
        "clinicaltrialsgov-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about ClinicalTrials.gov MCP in Pydantic AI

The framework compares the tool output against your Pydantic models. If the registry data doesn't fit your schema, the agent raises a validation error.

You install the MCP support package and pass the server URL to the MCPToolset. It integrates with your agent's existing toolset list.

Yes, the framework is model-agnostic. Your Pydantic models ensure that regardless of the LLM, the trial data remains consistent.

You manage the server externally and pass the endpoint. The framework handles the transport, and you can catch connection failures in your own logic.

We enforce strict transport security. Your search parameters and registry results are processed in memory and deleted immediately after the session.

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