Stop running agents in the dark. Implement structured observability for AI agents — trace every tool call, track token costs, detect infinite loops, and build a real-time monitoring dashboard.

Test your MCP server with automated tests, publish it to npm/GitHub, register on community directories, and explore the broader MCP ecosystem of clients, tools, and best practices.

Secure your MCP server for production. API key authentication, Express middleware, rate limiting, CORS policies, environment validation, graceful shutdown, and Docker security hardening.

Take your MCP server from localhost to remote. Full SSE transport implementation with Express.js, Docker multi-stage build, environment configuration, health checks, MCP Inspector over SSE, and connecting Claude Desktop remotely.

Add Resources and Prompts to your MCP server. Expose issue comments as readable content, create reusable prompt templates, add advanced tools with pagination and streaming, and test all three capabilities together with MCP Inspector.

Step-by-step tutorial: Build your first MCP server from absolute zero. Learn the MCP protocol architecture, server lifecycle, project setup with Node.js/TypeScript, your first tool, and testing with MCP Inspector.

The final post in the Agent Security series: how to assemble everything into a production defense stack. Defense in depth for AI agents, security architecture patterns, incident response for agent breaches, monitoring and alerting, and a complete production-ready security configuration.

How to audit AI agents for compliance frameworks. Audit trails, conversation logging, explainability, data retention, PII handling, SOC2 controls for agents, GDPR right to explanation, and PCI compliance for agent payment processing.