AACT Clinical Trials▌
by navisbio
Integrate with AACT Clinical Trials for robust querying and analysis of large-scale clinical trial data for research and
Integrates with the AACT clinical trials database, enabling querying and analysis of large-scale trial data for research and healthcare applications.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Medical researchers analyzing clinical trial trends
- / Pharmaceutical companies conducting competitive intelligence
- / Healthcare analysts studying treatment outcomes
- / Academic institutions performing meta-analyses
capabilities
- / Execute SQL queries on clinical trials data
- / Browse 70+ clinical trial database tables
- / Search columns across all tables by keyword
- / Aggregate and analyze trial outcomes and metrics
- / Filter trials by phase, condition, sponsor, or location
- / Export query results with pagination
what it does
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.
about
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.
how to install
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.
license
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.
readme
AACT Clinical Trials MCP Server
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.
Why AACT over the ClinicalTrials.gov API?
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.
Tools
| 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.
Setup
- Create a free account at https://aact.ctti-clinicaltrials.org/users/sign_up
- Install the plugin (see options below)
- Enter your AACT credentials when prompted
Installation
Option 1: Claude Desktop Plugin (recommended)
Download the latest .mcpb file from Releases and open it in Claude Desktop. You'll be prompted for your AACT credentials.
Option 2: Published package
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"
}
}
}
}
Option 3: Docker
{
"mcpServers": {
"aact": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"--env", "DB_USER=your_username",
"--env", "DB_PASSWORD=your_password",
"navisbio/mcp-server-aact:latest"
]
}
}
}
Option 4: From source
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"
}
}
}
}
Example Prompts
1. Competitive landscape analysis
"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.
2. Drug pipeline search
"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.
3. Endpoint analysis
"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.
4. Geographic distribution
"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.
Privacy
This server is read-only and does not collect or store any personal data. See PRIVACY.md for details.
Troubleshooting
Connection or authentication errors
- Verify your AACT credentials at https://aact.ctti-clinicaltrials.org/users/sign_in
- The AACT database undergoes weekly maintenance (typically weekends) — try again later if the connection is refused
- Ensure
DB_USERandDB_PASSWORDare set correctly in your config
spawn uvx ENOENT error
The 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"
}
}
}
}
Contributing
- Open an issue on GitHub
- Email: [email protected]
License
MIT
FAQ
- What is the AACT Clinical Trials MCP server?
- AACT Clinical Trials is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for AACT Clinical Trials?
- This profile displays 68 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Direct Database Queries from AI
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
Data Analysis & Reporting
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
Schema Exploration
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Data Validation & Quality Checks
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor with MCP support
- ›Database credentials (read-only recommended for safety)
- ›Network access from Claude client to database
- ›Understanding of database security and access control
Time Estimate
15-30 minutes including configuration and testing
Installation Steps
- 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 8.Document query patterns for team use
Troubleshooting
- ⚠Connection refused: Check database is running and network accessible
- ⚠Authentication failed: Verify credentials, check user permissions
- ⚠Claude can't see tables: Grant appropriate read permissions to database user
- ⚠Slow queries: Add indexes, limit result set size, use read replicas
- ⚠MCP server not loading: Check config syntax, restart Claude Desktop
Best Practices▌
✓ Do
- +Use read-only database credentials to prevent accidental writes
- +Connect to read replica, not production primary database
- +Set query timeout limits to prevent long-running queries
- +Document database schema and common queries for AI context
- +Monitor query performance and optimize slow queries
- +Use connection pooling for better performance
- +Test with non-production data first
✗ Don't
- −Don't use production write credentials—risk of data corruption
- −Don't query production database during peak traffic hours
- −Don't expose sensitive PII without proper access controls
- −Don't skip query result validation—AI can misinterpret schema
- −Don't allow unlimited result set sizes—set LIMIT clauses
- −Don't share database credentials in plain text config files
💡 Pro Tips
- ★Create database views for common queries to simplify AI access
- ★Add schema comments/descriptions so AI understands column meanings
- ★Use semantic table/column names ('customer_lifetime_value' not 'clv')
- ★Set up query logging to audit what Claude is querying
- ★Create saved query templates for recurring analysis
- ★Combine with data visualization tools for better insights
Technical Details▌
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
- Model Context Protocol (MCP)
- Database-specific protocols (PostgreSQL, MySQL, MongoDB)
Compatibility
- PostgreSQL
- MySQL
- SQLite
- MongoDB
- Redis
When to Use This▌
✓ 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.
Integration▌
- →Read replica connection for analytics queries
- →Database view layer to abstract complex joins
- →Query result caching for repeated questions
- →Audit logging of all AI-generated queries
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★68 reviews- ★★★★★Soo Kim· Dec 28, 2024
AACT Clinical Trials is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Luis Kim· Dec 24, 2024
AACT Clinical Trials is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Nia Abbas· Dec 20, 2024
AACT Clinical Trials reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Nia Mensah· Dec 16, 2024
We wired AACT Clinical Trials into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Soo Jackson· Dec 12, 2024
According to our notes, AACT Clinical Trials benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Harper Bhatia· Dec 8, 2024
AACT Clinical Trials is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Nia Okafor· Dec 4, 2024
Strong directory entry: AACT Clinical Trials surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Daniel Diallo· Nov 27, 2024
We evaluated AACT Clinical Trials against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Nia Nasser· Nov 19, 2024
I recommend AACT Clinical Trials for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Advait Kim· Nov 15, 2024
We evaluated AACT Clinical Trials against two servers with overlapping tools; this profile had the clearer scope statement.
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