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Understanding Security in MCP

Security within the Model Context Protocol (MCP) presents a dual challenge. It requires addressing both traditional web vulnerabilities and a new class of threats specific to AI agents. As MCP standardizes how agents interact with external tools and data, it inevitably creates new attack surfaces that demand a comprehensive, defense-in-depth security strategy. Properly securing an …

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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 …

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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 …

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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 …

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MCP Server

An MCP server is the component of the Model Context Protocol (MCP) that exposes tools, data, or resources in a standardized format so they can be consumed by an MCP client and used by an AI agent. It makes capabilities discoverable, interoperable, and reusable across different AI systems without custom integration code. Key characteristics The …

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Agentic System

An agentic system is an AI system built around one or more AI agents that autonomously pursues goals through reasoning, planning, and tool use. While an AI agent represents a single reasoning entity, an agentic system includes the supporting infrastructure such as tool interfaces, integration layers (including MCP client and MCP server), hosts, and the …

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Large Language Model

A large language model (LLM) is a type of AI model trained on massive amounts of text data to predict the most likely next token in a sequence. This simple mechanism enables LLMs to generate fluent text, translate languages, write code, answer questions, and perform a wide variety of reasoning-like tasks. Key characteristics LLMs are …

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Orchestration Layer

An orchestration layer is the coordinating logic that connects LLMs, AI agents, and external components into a reliable AI system. It ensures that tool calls, workflows, and results flow in the right order, with the right safeguards, so the overall system behaves predictably at scale. Key characteristics Unlike an AI agent, which makes decisions about …

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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 …

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Understanding Provenance in MCP

In today’s complex AI landscape, provenance in the Model Context Protocol (MCP) is the mechanism that ensures transparency and accountability. It functions as a detailed history for AI workflows, tracking the origin of data, the transformations it undergoes, and the actions performed by AI agents. This comprehensive audit trail is essential for understanding an agent’s …