Secure AI Apps with MCP & Auth0 FGA Integration

Modern AI applications demand robust security frameworks to control access to sensitive tools and data. This tutorial demonstrates how to integrate the Model Context Protocol (MCP) with Auth0 FGA to enforce granular permissions in AI systems. By the end, you’ll build a TypeScript server that dynamically restricts tool access based on user roles and document visibility rules.

Why MCP and FGA Matter for AI Security

MCP acts as a universal API for AI systems, enabling seamless integration with external tools. However, without access control, it risks exposing sensitive functions to unauthorized users. Auth0 FGA solves this by implementing Relationship-Based Access Control (ReBAC), inspired by Google’s Zanzibar model. Together, they create a secure, scalable architecture for AI applications.

Key Benefits of This Integration

• Dynamic tool visibility based on user permissions
• Granular document access control
• Open-source flexibility with TypeScript

Prerequisites for the Tutorial

Before starting, ensure you have:

• Node.js 18+
• Auth0 tenant (free account available)
• Auth0 FGA account (free tier supported)
• Git repository with starter code

Application Overview

The MCP server will expose three tools:

1. get_whoami: Identifies the authenticated user
2. get_datetime: Returns current UTC time
3. get_documents: Lists documents with access filtering

Step-by-Step Implementation

1. Project Setup

Clone the starter repository and install dependencies:

git clone [repository-url]
git switch 01-starter
npm install

2. Tool Registration

Create tools.ts to define MCP functions:

import { FastMCP } from 'fastmcp';
import { DocumentApi } from './documentApi';

const documentApi = new DocumentApi();

export function registerTools(mcpServer: FastMCP) {

// Add tools here
}

3. Basic Tool Implementation

Start with simple functions before adding security:

mcpServer.addTool({

name: 'get_whoami',

execute: async () => ({

content: [{ type: 'text', text: 'Authentication pending' }]

})
});

4. Document Access Control

Use FGA to filter documents by visibility:

mcpServer.addTool({

name: 'get_documents',

execute: async () => {

const documents = await documentApi.getDocuments(false);

return {

content: [{

type: 'text',

text: JSON.stringify({

count: documents.length,

filter: 'public',

documents

})

}]

};

}
});

Testing Your Secure MCP Server

Start the server with:

npm run start

Use MCP Inspector to test endpoints:

npx @modelcontextprotocol/inspector@latest

Expected Output

Inspector will show:

• Public document list
• UTC timestamp
• User authentication status

Conclusion and Next Steps

This tutorial demonstrates how to secure AI tools with MCP and FGA. The final implementation ensures users only see authorized functions and documents. To expand this solution:

• Integrate OAuth2 for user authentication
• Add audit logging for access events
• Implement role-based document tagging

Ready to build secure AI systems? Clone the repository and follow the tutorial to experience first-hand how MCP and FGA work together. Your applications will gain enterprise-grade access control while maintaining AI flexibility.

FAQ

How does MCP with Auth0 FGA improve AI security?

FGA adds fine-grained permissions to MCP tools, ensuring users only access authorized functions and data.

Can I use this setup with other authentication providers?

Yes, the architecture supports any OAuth2-compliant provider by modifying the Auth0 integration.

What if I need to manage thousands of documents?

FGA scales efficiently with its ReBAC model, handling complex access rules across large datasets.

Is TypeScript required for this implementation?

While the example uses TypeScript, you can adapt the core logic to JavaScript with minor syntax changes.

How do I monitor access violations?

Implement logging middleware to track unauthorized access attempts and generate alerts.