Carlisia Campos

MCP Technical Strategist

Carlisia Campos is a senior software engineer specializing in Go, cloud-native systems, AI (LLM/MCP) integration, and open source. A former maintainer of Velero and Knative, she is self-funding a new venture, Grokking Tech, so that she can explore and author original methodologies for practical and secure MCP adoption. Carlisia has co-hosted The Podlets and Go Time podcasts, spoken at KubeCon, GopherCon and Women Transforming Technology conferences, and continues to support mentorship, accessibility, and innovation in open source.

Contributed Resources (20)

Article

Understanding Consent in MCP

In the Model Context Protocol (MCP), consent is the foundational security mechanism that governs how to delegate authority to AI agents. It ensures any actions they take are authorized, traceable, and revocable. Think of consent as a detailed, enforceable contract that spells out exactly what agents can do on the user’s behalf, going far beyond …

Article

The Practical Guide to MCP Server Adoption

This framework helps you figure out whether MCP servers are right for your specific use case by systematically checking eight critical constraint categories. Each constraint gets scored, and the combined assessment guides your decision, a true compatibility test for you and MCP. The eight constraint categories What’s included in this framework Constraint assessment matrix For …

Article

The Strategic Case for Building MCP Servers

The rapid evolution of Large Language Models (LLMs) presents significant architectural challenges for engineers. As we build integrations that AI systems consume, ensuring reliable and secure access to external systems (databases, APIs, and third-party services) is a primary concern. The Model Context Protocol (MCP) addresses these challenges by providing a standardized, composable, and secure framework …

Article

The Agentic Intention Framework In Practice

Overview: Who, what and why WHO will use the framework: WHAT are the components of the framework: A structured framework that transforms vague AI agent ideas into precise, measurable workflow definitions by eliciting explicit answers to critical questions before development begins: Framework constraints WHY use the framework: Achieve higher adoption rates for agentic tools and …

Article

Composition Patterns That Work

One of MCP’s most powerful features is enabling LLMs to combine tools into solutions. In this section I want to give you ideas for how to craft effective composition patterns. Three principles for composable tools 1. Predictable contracts Each tool has clear inputs and outputs. When AnalyzeDocumentationQuality returns a QualityReport, the LLM knows exactly what …

Article

Defining Tighter Boundaries Using Intention

The agentic intention framework can be used as a daily decision-making filter. If every choice we face flows through this framework, we will succeed in keeping our MCP servers focused and effective. Scope boundaries Before writing any code, our intention helps us define what belongs in our MCP server and what doesn’t. The Goldilocks test …

Article

A Tale of Three MCP Servers

Let me tell you about Eduardo and Monica (😬 if you know, you know, lolz), and Bruno, three developers who learned this lesson very differently. Keep in mind: A tool (handler) can either contain all the code to be executed or simply call another function that serves as an entry point for the actual work. …

Article

The Agentic Intention Framework

Introduction: The paradox of building with AI in the loop As modern software engineers, we’re trained in the agile way: ship fast, iterate based on feedback, let design emerge. We start minimal and evolve based on what we learn from users. But architecting robust, performant, and useful MCP servers demands the opposite: with LLMs in …