I have authored a comprehensive body of work on the Model Context Protocol (MCP), a series of interconnected articles to guide developers through a complete adoption journey.
This work was contracted to address a critical need in the emerging AI ecosystem. As MCP was introduced in late 2024, a great number of developers understand its promise but struggle with finding guidance on how and when to adopt it purposefully.
This content aims to fill that gap with frameworks, decision tools, and implementation guides.
Developer empathy and honest trade-offs
The content doesn’t treat MCP as a silver bullet. It acknowledges challenges and limitations rather than promoting MCP universally. The adoption guide in particular explicitly identifies use cases where MCP is not appropriate. The intention article highlights how building for AI systems require a process mindset different from our good ol’ agile. Most examples include failure modes and what can go wrong.
Enterprise perspective
The articles speak directly to engineering leaders making business-impact decisions. Concerns addressed include token economics, infrastructure costs, compliance requirements, operational capacity, risk management, and measurable outcomes such as latency reduction and overall cost savings. The goal is to make MCP adoption a strategic choice, not a leap of faith.
Original frameworks created
This collection introduces original frameworks and approaches I created that add value beyond summarizing existing knowledge. It introduces new ways to think about MCP server adoption and design.
The 8-constraint adoption framework
The MCP server practical guide for adoption introduces a decision framework with a quantitative scoring model to evaluate whether MCP fits your use case and, if so, under what conditions. The framework scores eight critical dimensions:
- Performance & Latency Requirements
- Security & Risk Tolerance
- Token Economics & Cost Structure
- Operational Complexity & Team Capacity
- Data Locality & Compliance
- Scalability & Resource Constraints
- Integration Complexity & Technical Fit
- Ecosystem Maturity & Vendor Risk
Intention: To enable engineers to have informed conversations with stakeholders using quantitative data rather than intuition or hype.
The intention statement framework
The Role of intention in architecting MCP servers article includes the agentic intention framework and introduces a new mental model for building tools that LLMs can use effectively. It’s based on the insight that, with LLMs in the loop, system effectiveness is significantly improved by intentionally and explicitly defining constraints upfront.
This content helps developers articulate:
- what problem the server solves (specific, measurable)
- what is explicitly in scope (concrete capabilities)
- what is explicitly out of scope (clear boundaries)
- who is the primary user (LLM, human, or agent)
- what are the key constraints (performance, security, cost)
- how tools should compose with others (integration patterns).
Intention: To provide a template developers can use immediately to define server purpose, scope, and constraints. The distinction between human and agent client types is significant to understand different use cases.
Content architecture: an adoption journey
The article collection covers a complete developer journey from initial awareness to implementation.
Awareness: discovering the Agentic ecosystem
Introductory glossary defines AI-related concepts such as AI system, agentic system, AI tool, orchestration layer, MCP host, and others. It establishes a shared vocabulary that all articles refer to.
These entries establish baseline knowledge with clear definitions, characteristics, examples, and relationship mappings.
Understanding: the strategy
The strategic case for building MCP servers article provides the foundational argument for why developers should adopt the MCP protocol, positioning it as a response to fundamental challenges in AI infrastructure. It addresses architectural pain points: integration fragmentation, context delivery chaos, tool interface drift, security gaps, and deployment complexity.
The article articulates why standardization matters, explaining a 6-step client-server interaction model.
Evaluation: making the decision
The MCP server practical guide for adoption, which includes the 8-constraint framework, helps engineers decide when MCP is (and isn’t) worth adopting. It brings objectivity to what can often be a hype-driven decision.
Design: architecting the server
The Role of intention in architecting MCP servers article is a deep-dive article that introduces the agentic intention framework, showing how to design MCP servers for clarity, composition, and long-term maintainability.
At 28 minutes reading time, this is the most detailed article in the collection.
Implementation: example guidance
The Kubernetes domain is familiar to many engineers, making the concepts in the Building an Intelligent Kubernetes Management System with an MCP Server article immediately applicable.
The article includes specific implementation guidance: start with read-only tools, implement comprehensive logging, design for graceful degradation, and build feedback loops. It also introduces a intelligence layer model: a Kubernetes-based reference system for reasoning vs. execution separation.