@medikode/mcp-server

Model Context Protocol (MCP) server for Medikode's AI-driven medical coding platform. This package enables AI assistants like Claude Desktop, Cursor, and ChatGPT to access Medikode's medical coding tools directly.

Medikode's AI-driven medical coding platform dashboard showing API usage trends and analytics

🌟 Features

• 5 Powerful MCP Tools: Validate codes, QA charts, parse EOBs, calculate RAF scores, and more

• AI Assistant Integration: Works with Claude Desktop, Cursor, ChatGPT, and other MCP-compatible clients

• Secure: Uses your existing Medikode API keys with the same security and access controls

• Fast: Direct API access with caching for optimal performance

• Easy Setup: Simple configuration with npx - no local installation required

🚀 Quick Start

Installation

Configuration

Claude Desktop

Add to your claude_desktop_config.json:

Cursor IDE

Add to your cursor_settings.json:

🛠 Available Tools

1. validate_codes

Validates CPT/ICD-10 codes against clinical documentation.

Inputs:

• chart_text (string, required): Provider note or chart excerpt

• codes (array[string], required): CPT/ICD-10 codes to validate

Outputs:

• valid (boolean): Whether codes are valid for the chart

• recommendations (array[string]): Missing or conflicting codes

2. qa_chart

Performs a coding quality assurance check.

Inputs:

• chart_text (string, required): Clinical documentation to review

Outputs:

• issues_found (array[string]): Documentation or coding gaps

• suggested_codes (array[string]): Recommended additional codes

3. parse_eob

Extracts structured data from Explanation of Benefits (EOB) documents.

Inputs:

• eob_content (string, required): Raw EOB text (or PDF extracted text)

Outputs:

• payer (string): Insurance payer name

• claim_number (string): Claim reference number

• total_billed (number): Total amount billed

• total_allowed (number): Total amount allowed by payer

• insurance_paid (number): Amount paid by insurance

• patient_responsibility (number): Patient out-of-pocket amount

4. score_raf

Calculates RAF score and HCC capture from encounter documentation.

Inputs:

• chart_text (string, required): Clinical encounter documentation

Outputs:

• raf_score (float): Risk Adjustment Factor score

• hcc_codes (array[string]): Hierarchical Condition Category codes

5. multi_validate

Composite workflow that validates chart coding and calculates RAF in one step.

Inputs:

• chart_text (string, required): Clinical documentation

• codes (array[string], optional): Optional codes to validate

Outputs:

• validation_results (object): Results from validate_codes

• raf_results (object): Results from score_raf

💡 Example Usage

Once configured, you can use the tools in your AI assistant:

🔑 Authentication

All tools require a valid Medikode API key. You can obtain one by:

1. Signing up at medikode.ai

2. Generating an API key in your account settings

3. Setting the MEDIKODE_API_KEY environment variable

📊 Usage Tracking

All MCP tool usage is tracked and appears in your Medikode dashboard alongside regular API calls. This includes:

• Number of API calls made

• Charts processed

• EOBs parsed

• RAF scores calculated

🔧 Troubleshooting

Common Issues

MCP Server Not Found

• Ensure Node.js and npm are installed

• Verify the package is available via npx: npx @medikode/mcp-server --help

Authentication Errors

• Check that your API key is correct and active

• Verify the MEDIKODE_API_KEY environment variable is set

• Ensure your API key has the required permissions

Tool Not Available

• Restart your AI client after configuration changes

• Verify the MCP server configuration is correct

• Ensure your AI client supports MCP

📚 Documentation

• Medikode Documentation

• MCP Tools Guide

• API Reference

🛠 Development

Prerequisites

• Node.js 18.0.0 or higher

• npm or yarn

• Medikode API key

Local Development

1. Clone the repository:

   git clone https://github.com/medikode/mcp-server.git cd mcp-server

2. Install dependencies:

   npm install

3. Set up environment variables:

   cp env.example .env # Edit .env with your API key

4. Run in development mode:

   npm run dev

5. Test the MCP server:

   npm run test:routing

Building

Testing

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Workflow

1. Fork the repository

2. Create a feature branch: git checkout -b feature/amazing-feature

3. Commit your changes: git commit -m 'Add amazing feature'

4. Push to the branch: git push origin feature/amazing-feature

5. Open a Pull Request

Code Style

• Use ESLint for JavaScript linting

• Follow the existing code style

• Add tests for new features

• Update documentation as needed

🐛 Bug Reports

Found a bug? Please open an issue with:

• Clear description of the problem

• Steps to reproduce

• Expected vs actual behavior

• Environment details (Node.js version, OS, etc.)

💡 Feature Requests

Have an idea for a new feature? We'd love to hear it! Please open an issue with:

• Clear description of the feature

• Use case and benefits

• Any implementation ideas you have

📊 Changelog

See CHANGELOG.md for a list of changes and version history.

🤝 Support

• Issues: GitHub Issues

• Documentation: docs.medikode.ai

• Email: support@medikode.ai

• Discord: Join our community

📄 License

ISC License - see LICENSE file for details.

🔗 Links

• Website

• Documentation

• MCP Tools

• GitHub

• npm Package

🙏 Acknowledgments

• Built with Model Context Protocol

• Powered by Medikode medical coding platform

• Thanks to all our contributors and users!