by navisbio
Integrate with AACT Clinical Trials for robust querying and analysis of large-scale clinical trial data for research and
Query the AACT clinical trials database using SQL to analyze trial data from ClinicalTrials.gov. Access 70+ structured tables containing studies, interventions, outcomes, sponsors, and facilities.
AACT Clinical Trials is a community-built MCP server published by navisbio that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with AACT Clinical Trials for robust querying and analysis of large-scale clinical trial data for research and It is categorized under databases, analytics data.
You can install AACT Clinical Trials in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
AACT Clinical Trials is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
AACT Clinical Trials is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
AACT Clinical Trials is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
AACT Clinical Trials reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired AACT Clinical Trials into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, AACT Clinical Trials benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
AACT Clinical Trials is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: AACT Clinical Trials surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated AACT Clinical Trials against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend AACT Clinical Trials for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated AACT Clinical Trials against two servers with overlapping tools; this profile had the clearer scope statement.
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Query the AACT (ClinicalTrials.gov) database directly from Claude. Explore 70+ tables of clinical trial data — studies, interventions, outcomes, sponsors, facilities — using read-only SQL with buffered pagination.
The ClinicalTrials.gov API returns one JSON record per trial — useful for quick lookups, but awkward for analytics. Want the average duration of Phase 2 NSCLC trials from 2020-2025? With the API you'd filter trials, extract dates from each JSON record, then compute durations client-side. With AACT, that's a single SQL query.
A structured PostgreSQL database makes it far easier to aggregate, combine, and summarize clinical trial data in any way you need. And for AI-assisted analysis, SQL is a standard that LLMs handle extremely well — fewer mistakes, less context to manage, better performance, and lower cost compared to parsing bespoke API responses.
Note: This is an independent, third-party integration. It is not affiliated with or endorsed by the Clinical Trials Transformation Initiative (CTTI) or Duke University. However, we released a case study with CTTI on integrating their database with Claude - see AACT case study.
| Tool | Description |
|---|---|
database_info | Confirm database connection, server time, and data currency |
list_tables | Discover all available tables with approximate row counts |
describe_table | Inspect column names, types, distinct counts, and sample values |
get_column_values | Get distinct values for a column with counts — essential before filtering |
search_columns | Find columns by keyword across all tables (e.g. masking -> designs.masking) |
read_query | Execute a SELECT, CTE, or EXPLAIN query with buffered results and preview |
fetch_rows | Page through buffered query results without re-querying |
All tables join on nct_id.
Download the latest .mcpb file from Releases and open it in Claude Desktop. You'll be prompted for your AACT credentials.
Add to your claude_desktop_config.json (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": {
"aact": {
"command": "uvx",
"args": ["mcp-server-aact"],
"env": {
"DB_USER": "your_username",
"DB_PASSWORD": "your_password"
}
}
}
}
{
"mcpServers": {
"aact": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"--env", "DB_USER=your_username",
"--env", "DB_PASSWORD=your_password",
"navisbio/mcp-server-aact:latest"
]
}
}
}
git clone https://github.com/navisbio/mcp-server-aact.git
cd mcp-server-aact
uv sync
{
"mcpServers": {
"aact": {
"command": "uv",
"args": ["run", "--directory", "/path/to/mcp-server-aact", "mcp-server-aact"],
"env": {
"DB_USER": "your_username",
"DB_PASSWORD": "your_password"
}
}
}
}
"Who are the top 10 sponsors of Phase 3 Alzheimer's disease trials? Break down by trial status."
The server will discover relevant tables, check enum values for phase and status, then build a query joining studies, conditions, and sponsors.
"Find all actively recruiting Phase 2 and Phase 3 trials for pembrolizumab in non-small cell lung cancer. Show NCT ID, title, enrollment, and lead sponsor."
Uses get_column_values to confirm phase format (PHASE2, PHASE3), then queries across studies, browse_interventions, and conditions.
"What are the most common primary outcome measures in completed Phase 3 type 2 diabetes trials?"
Joins studies with outcomes to analyze endpoint patterns, grouped by outcome measure type.
"How many clinical trial sites does a typical rare disease trial have? Show the top countries by site count."
Queries the facilities table joined with conditions to map trial geography.
This server is read-only and does not collect or store any personal data. See PRIVACY.md for details.
DB_USER and DB_PASSWORD are set correctly in your configspawn uvx ENOENT errorThe system cannot find uvx. Use the full path:
{
"mcpServers": {
"aact": {
"command": "/Users/username/.local/bin/uvx",
"args": ["mcp-server-aact"],
"env": {
"DB_USER": "your_username",
"DB_PASSWORD": "your_password"
}
}
}
}
MIT
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ Use when
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid when
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.