# Enterprise Clinical Trials Agent (MCP Architecture)
This project implements a robust Retrieval-Augmented Generation (RAG) agent using the **Model Context Protocol (MCP)** standard. It acts as an integration middleware that fetches real-world clinical data, stores it in hybrid databases (SQL + Vector), and exposes it to LLM clients for autonomous reasoning.
## 🚀 Key Features
- **REST API Integration:** Implements a custom ETL pipeline (`etl_pipeline.py`) that consumes the ClinicalTrials.gov API with pagination and error handling.
- **Hybrid Data Architecture:**
- **Structured (SQL):** Uses SQLite for high-precision filtering (Trial Status, Phase, Conditions).
- **Unstructured (Vector DB):** Uses ChromaDB with `sentence-transformers` for semantic search within medical protocols.
- **MCP Server:** A centralized integration server (`mcp_server.py`) that standardizes tools for any AI client (Claude Desktop, Cursor, IDEs).
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## 📸 Autonomous Agent Demo
### 1. Backend Verification (MCP Inspector)
*The server exposes structured SQL tools and Vector RAG tools via standard JSON-RPC.*
### 2. Structured Reasoning (SQL Tool)
*User asks for "Diabetes trials". The Agent autonomously selects the SQL tool to filter by disease.*
### 3. Semantic Reasoning (RAG Tool)
*User asks for specific "kidney function exclusion criteria". The Agent switches tools to perform vector search on the protocol text.*
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## 🛠️ Installation
1. **Clone the repository:**
git clone https://github.com/tonih23/clinical-trials-mcp-agent.git
cd clinical-trials-mcp-agent
2. **Set up the environment:**
python -m venv venv
# Windows:
venv\Scripts\activate
# Mac/Linux:
source venv/bin/activate
pip install -r requirements.txt
## 🔄 Usage Workflow
### 1. Data Ingestion (ETL)
Run the pipeline to fetch live data from the API and populate local databases. This demonstrates the REST integration pattern:
python etl_pipeline.py
This will fetch the latest trials related to Oncology, Cardiology, and Diabetes.
### 2. Run the MCP Server (Dev Mode)
Start the integration server to test connections:
mcp dev mcp_server.py
### 3. Connect to LLM Client (Production-like)
To use this with an MCP-compliant client (like Claude Desktop), add this configuration to your `claude_desktop_config.json`:
> Note: Replace the path below with the absolute path to your project folder.
> On Windows, use double backslashes (`\\`).
{
"mcpServers": {
"clinical-agent": {
"command": "python",
"args": ["C:\\Users\\YOUR_USER\\Desktop\\clinical-trials-mcp-agent\\mcp_server.py"]
}
}
}
## 🧠 Example Queries
Once connected, the Agent can perform autonomous tool calling:
### Structured Query (SQL)
"Find active Phase 3 trials for Diabetes."
(The LLM will automatically route this to `search_trials_sql`.)
### Semantic Query (RAG)
"For trial NCT00528879, what are the specific exclusion criteria regarding kidney function?"
(The LLM will automatically route this to `get_protocol_details_rag`.)
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Developed as a Proof of Concept for Enterprise AI Integration.
