The Context Bloat Problem with MCP
Model Context Protocol (MCP) serves as a standardized bridge, allowing Large Language Models (LLMs) to interact with external APIs and file systems using natural language. While it effectively connects models to the "outside world," it introduces a significant architectural trade-off: context bloat. Because MCP requires defining tools within the model's active definition, equipping an agent with numerous tools forces the model to process hundreds of lines of irrelevant instructions for every interaction. As the number of tools grows, the model's memory and performance suffer because it cannot selectively ignore the definitions of tools it isn't currently using.
Progressive Disclosure via Skills
Skills act as a complementary layer to MCP, focusing on reusability and efficiency through a concept called "progressive disclosure." A Skill is essentially a saved prompt—often a simple markdown or text file—that can also encapsulate scripts or command-line instructions.
Unlike MCP, which loads all tool definitions upfront, Skills utilize a three-part structure to optimize memory:
- Frontmatter: A summary of the skill and its purpose, which the agent loads into its internal registry upon startup.
- Body: Human-readable English instructions detailing how the model should execute the task.
- Optional Linked File: Deterministic scripts or external tools that are only invoked when the agent determines they are necessary.
By only loading the frontmatter initially, the agent maintains a lightweight registry of capabilities. It only pulls in the full instructions and scripts when it identifies a specific need, preventing the performance degradation seen in monolithic tool definitions.
Programming in English
Beyond technical efficiency, Skills democratize agent development. Because they are written in natural language, they allow non-developers to define, iterate, and share business logic across teams. This shift treats English as a programming language, enabling builders to capture complex workflows in reusable, shareable prompts. MCP and Skills are not mutually exclusive; in practice, a robust agent often uses Skills as the "instructional layer" to manage and orchestrate the external tools provided by MCP.