Healthcare CPT Code Analysis — MCP Server

An MCP (Model Context Protocol) server that connects Claude Desktop to a healthcare claims database, enabling natural-language analysis of CPT codes, reimbursement rates, payer performance, and denial patterns.

Built to demonstrate how MCP bridges the gap between AI assistants and domain-specific data systems in the healthcare/PBM space.

Architecture

Claude Desktop                    MCP Server (Python)                SQLite Database
┌──────────────┐     JSON-RPC    ┌─────────────────┐               ┌───────────────┐
│              │◄──── stdio ────►│                 │◄── queries ──►│ cpt_codes     │
│  User asks   │                 │  6 Tools        │               │ payers        │
│  questions   │                 │  3 Resources    │               │ payer_rates   │
│  in plain    │                 │  2 Prompts      │               │ claims (10K)  │
│  English     │                 │                 │               │ denial_reasons│
└──────────────┘                 └─────────────────┘               └───────────────┘

How it works:

1. Claude Desktop starts this server as a subprocess

2. The server announces its tools (functions Claude can call)

3. When you ask a question, Claude decides which tool(s) to use

4. The server queries SQLite and returns structured results

5. Claude interprets the data and responds in natural language

Related MCP server: MCPDB - Database Access MCP Server

What's Inside

Tools (Functions Claude Can Call)

Resources (Context Claude Can Read)

• schema://claims-database — Full database schema with column descriptions

• schema://cpt-categories — CPT code category reference with rate ranges

• data://summary-stats — Quick overview of the entire dataset

Prompts (Structured Analysis Templates)

• analyze-claim — Step-by-step analysis of a specific claim

• rate-review — Comprehensive payer/category rate review for contract negotiations

Dataset

• 70 CPT codes across 5 categories (E&M, Surgery, Radiology, Pathology, Medicine)

• 7 payers (5 commercial + Medicare + Medicaid)

• 10,000 claims with realistic denial patterns (~15% denial rate)

• 490 payer rate schedules (7 payers × 70 codes)

• Medicare rates based on 2024 CMS Physician Fee Schedule

• CARC denial reason codes from real 835 remittance standards

Setup

Prerequisites

• Python 3.11+

• Claude Desktop (with MCP support)

Install

git clone https://github.com/Ishaan24687/cpt-analysis-mcp-server.git
cd cpt-analysis-mcp-server
pip install -r requirements.txt

Seed the Database

python -m src.seed_data

This creates cpt_analysis.db with all reference data and synthetic claims.

Connect to Claude Desktop

1. Open Claude Desktop settings

2. Go to Developer > Edit Config

3. Add the server config from claude_desktop_config.json:

{
  "mcpServers": {
    "cpt-analysis": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/cpt-analysis-mcp-server"
    }
  }
}

4. Restart Claude Desktop

5. You should see "cpt-analysis" in the MCP tools list (hammer icon)

Demo Conversations

Once connected, try these in Claude Desktop:

Overview:

"Give me an overview of the claims data"

CPT Lookup:

"What is CPT 27447 and how much do different payers reimburse for it?"

Denial Investigation:

"Which CPT codes have the highest denial rates? What are the main reasons?"

Payer Comparison:

"Compare all payers for surgery procedures — who pays the best and who denies the most?"

Anomaly Detection:

"Find pricing anomalies where we're billing more than 3x the allowed amount"

Contract Negotiation Prep:

"I'm preparing for a rate negotiation with UnitedHealthcare for radiology services. Give me a complete analysis."

Project Structure

cpt-analysis-mcp-server/
├── src/
│   ├── __init__.py
│   ├── server.py          # MCP server — tools, resources, prompts
│   ├── database.py         # SQLite schema and connection management
│   └── seed_data.py        # Realistic healthcare data generation
├── claude_desktop_config.json
├── requirements.txt
├── .gitignore
└── README.md

Why MCP?

Traditional approach: Copy data from database → paste into ChatGPT → get generic answer.

MCP approach: Ask Claude a question → Claude queries your actual database → get a specific, data-backed answer.

MCP turns an AI assistant from a "smart text generator" into a "smart analyst with direct access to your systems." For healthcare operations teams dealing with claims data, this means faster root cause analysis, better contract negotiation prep, and real-time anomaly detection — all through natural conversation.

Tech Stack

• MCP SDK (mcp[cli]) — Protocol implementation

• SQLite — Zero-config database (production would use Azure SQL / PostgreSQL)

• Python 3.12 — Server runtime

• Claude Desktop — MCP host application