ClinicalTrials MCP Server

🔍 Enable AI assistants to search and access ClinicalTrials.gov data through a simple MCP interface.

The ClinicalTrials MCP Server provides a bridge between AI assistants and ClinicalTrials.gov's clinical trial repository through the Model Context Protocol (MCP). It allows AI models to search for clinical trials and access their content in a programmatic way.

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✨ Core Features

• 🔎 Trial Search: Query clinical trials with custom search strings or advanced search parameters ✅
• 🚀 Efficient Retrieval: Fast access to trial metadata ✅
• 📊 Metadata Access: Retrieve detailed metadata for specific trials using NCT ID ✅
• 📊 Research Support: Facilitate health sciences research and analysis ✅
• 📋 CSV Management: Save, load, and list CSV files with trial data ✅
• 🗃️ Local Storage: Trials are saved locally for faster access ✅
• 📊 Statistics: Get statistics about clinical trials ✅

🚀 Quick Start

Installing via Smithery

To install ClinicalTrials Server for Claude Desktop automatically via Smithery:

Claude

npx -y @smithery/cli@latest install ClinicalTrials-mcp-server --client claude --config "{}"

Cursor

Paste the following into Settings → Cursor Settings → MCP → Add new server:

• Mac/Linux

npx -y @smithery/cli@latest run ClinicalTrials-mcp-server --client cursor --config "{}" 

Windsurf

npx -y @smithery/cli@latest install ClinicalTrials-mcp-server --client windsurf --config "{}"

CLine

npx -y @smithery/cli@latest install ClinicalTrials-mcp-server --client cline --config "{}"

Installing Manually

Install using uv:

uv tool install ClinicalTrials-mcp-server

For development:

# Clone and set up development environment
git clone https://github.com/JackKuo666/ClinicalTrials-MCP-Server.git
cd ClinicalTrials-MCP-Server

# Create and activate virtual environment
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt

📊 Usage

Start the MCP server:

python clinical_trials_server.py

Once the server is running, you can use the provided MCP tools in your AI assistant or application. Here are some examples of how to use the tools:

Example 1: Search for clinical trials using a search expression and save to CSV

result = await mcp.use_tool("search_clinical_trials_and_save_studies_to_csv", {
    "search_expr": "COVID-19 vaccine efficacy",
    "max_studies": 5
})
print(result)

Example 2: Get studies by keyword

result = await mcp.use_tool("get_studies_by_keyword", {
    "keyword": "diabetes",
    "max_studies": 10
})
print(result)

Example 3: Get full study details for a specific trial

result = await mcp.use_tool("get_full_study_details", {
    "nct_id": "NCT04280705"
})
print(result)

Example 4: Search and save studies with custom fields

result = await mcp.use_tool("search_clinical_trials_and_save_studies_to_csv", {
    "search_expr": "alzheimer",
    "max_studies": 20,
    "filename": "alzheimer_studies.csv",
    "fields": ["NCT Number", "Study Title", "Brief Summary", "Conditions"]
})
print(result)

These examples demonstrate how to use the main tools provided by the ClinicalTrials MCP Server. Adjust the parameters as needed for your specific use case.

🛠 MCP Tools

The ClinicalTrials MCP Server provides the following tools:

search_clinical_trials_and_save_studies_to_csv

Search for clinical trials using a search expression and save the results to a CSV file.

Parameters:

• search_expr (str): Search expression (e.g., "Coronavirus+COVID")
• max_studies (int, optional): Maximum number of studies to return (default: 10)
• save_csv (bool, optional): Whether to

... View full README on GitHub