Advanced MCP: Enterprise-Grade AI Integration with Rust

The Problem We're Solving

Every day, knowledge workers in large organizations face the same frustrating pattern:

1. Ask ChatGPT or Copilot a question about their business data
2. Realize the AI doesn't have access to their systems
3. Open their database, CRM, or internal tools
4. Copy data, paste it into the AI conversation
5. Hope nothing sensitive gets leaked
6. Repeat dozens of times per day

This copy-paste workflow is:

• Inefficient: Hours lost to context switching
• Inconsistent: Different employees get different results
• Insecure: Sensitive data ends up in AI training sets
• Error-prone: Manual data transfer introduces mistakes

The Solution: Model Context Protocol

The Model Context Protocol (MCP) is an open standard that allows AI assistants to securely connect to your enterprise systems. Instead of copy-paste, your AI can:

• Query your databases directly (with proper authorization)
• Access your internal APIs and services
• Read documentation and knowledge bases
• Execute approved business workflows

All while maintaining enterprise security standards.

Why This Course?

There are plenty of tutorials showing how to build a "hello world" MCP server. This course is different.

We focus on enterprise requirements:

Why Rust?

When your MCP server handles sensitive enterprise data, you need:

• Memory safety: No buffer overflows or use-after-free bugs
• Performance: Microsecond response times, minimal cloud costs
• Reliability: If it compiles, it probably works correctly
• Type safety: Catch errors at compile time, not in production

Rust provides all of this, and the PMCP SDK makes it accessible even to developers new to Rust.

What You'll Build

By the end of this course, you'll have built:

1. A database MCP server that safely exposes SQL queries to AI
2. Deployed to three cloud platforms with full CI/CD
3. OAuth-protected endpoints integrated with your identity provider
4. Comprehensive test suites that run locally and in production
5. Observable infrastructure with logging, metrics, and alerting

More importantly, you'll understand the design principles that separate enterprise-grade MCP servers from toy examples.

Course Structure

Part I: Foundations

Start with the basics, but production-ready from day one. Build your first MCP server and understand the architecture.

Part II: Thoughtful Design

Learn why most MCP servers fail: too many confusing tools. Master the art of cohesive API design.

Part III: Cloud Deployment

Deploy to AWS Lambda, Cloudflare Workers, and Google Cloud Run. Connect real MCP clients.

Part IV: Testing

Generate tests from schemas, run them locally, then against production. Integrate with CI/CD.

Part V: Enterprise Security

Add OAuth authentication with Cognito, Auth0, and Entra ID. Implement proper token validation.

Part VI: AI-Assisted Development

Use Claude Code and other AI assistants to accelerate development of business logic.

Part VII: Observability

Add middleware for logging and metrics. Use pmcp.run for simplified monitoring.

Part VIII: Advanced Patterns

Compose multiple servers, build UIs, and architect for high availability.

Prerequisites

Before starting this course, you should have:

• Basic Rust knowledge (or willingness to learn)
• Access to a cloud account (AWS, GCP, or Cloudflare)
• An MCP client (Claude Desktop, VS Code, or similar)
• Familiarity with REST APIs and JSON

See the Prerequisites chapter for detailed setup instructions.

Let's Begin

Enterprise AI integration is no longer optional. Your competitors are already connecting their AI assistants to their data.

The question isn't whether to build MCP servers—it's whether to build them right.

Let's build them right.

Continue to Prerequisites →