How to Use the ClinicalTrials.gov MCP in LlamaIndex

Index live clinical trial data directly into your LlamaIndex vector stores to build grounded RAG applications for medical research.

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Connect ClinicalTrials.gov MCP to LlamaIndex

Create your Vinkius account to connect ClinicalTrials.gov to LlamaIndex 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 LlamaIndex

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Ingest registry data via MCP Server

Static PDFs fall short when a study updates its recruitment status overnight. The `search_clinical_trials` tool lets your LlamaIndex application pull fresh registry data by sponsor or condition. Instead of just reading the result, your setup indexes it. You transform live API responses into queryable nodes inside your existing knowledge base.

Vectorize specific trial protocols

When a user asks about a specific experimental therapy, they need exact details. Calling `get_trial_details` grabs the complete study record using its NCT identifier. LlamaIndex takes that structured output and embeds it alongside your internal medical documents. Now your semantic search covers both private research and public registry data simultaneously.

Build RAG pipelines for LlamaIndex enrollments

Matching patients to studies requires up-to-date availability. Your function agent uses `find_recruiting_trials` to locate open slots for specific diseases. The MCP standard ensures the schema matches what the framework expects. You bypass writing custom integration scripts and get straight to querying the embedded results.

Setup guide

Set up ClinicalTrials.gov MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all ClinicalTrials.gov MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to ClinicalTrials.gov tools.",
)
response = await agent.run("List recent ClinicalTrials.gov data")

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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 ClinicalTrials.gov. 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 ClinicalTrials.gov MCP in LlamaIndex

Grab the llama-index-tools-mcp package. Initialize BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and pass the async tool list to your FunctionAgent.

The search tool accepts drug names, keywords, and sponsor parameters. Your agent will pull those exact matches before indexing them.

If your document mentions an NCT ID, the agent can extract it and fetch the full protocol. It then grounds its final answer using the live registry data.

You decide how to route the tool responses. You can have the agent answer directly or pipe the JSON into your ingestion pipeline for embedding.

Your disease queries and drug keywords pass through a zero-trust Vinkius endpoint. The data hits the public registry, returns the trial details, and the ephemeral connection terminates immediately.

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