Recent advancements in the agent ecosystem have been centered around enhancing AI agents’ ability to interact with external tools and access domain-specific knowledge more efficiently. Two prominent approaches that have emerged are skills and Model Context Protocol (MCP). While they may seem similar initially, they differ in setup, task execution, and target audience. In this article, we will delve into the offerings of each approach and analyze their key distinctions.

Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open-source standard designed to enable AI applications to connect with external systems such as databases, local files, APIs, or specialized tools. It enhances the capabilities of large language models by providing tools, resources, and prompts that the model can utilize during reasoning. Essentially, MCP functions as a standardized interface, simplifying interactions between AI systems like ChatGPT or Claude and external data and services.

Setting up MCP servers is not overly complex, but it is primarily aimed at developers comfortable with concepts like authentication and command-line interfaces. Once configured, MCP facilitates highly structured interactions, with each tool performing a specific task and delivering a deterministic outcome based on the input. This reliability makes MCP ideal for tasks like web scraping, database queries, or API calls.

Typical MCP Flow

User Query → AI Agent → Calls MCP Tool → MCP Server Executes Logic → Returns Structured Response → Agent Uses Result to Answer the User

Limitations of MCP

While MCP offers a robust way for agents to engage with external systems, it also presents certain limitations. One key challenge is tool scalability and discovery, where agents need to navigate a growing number of MCP tools using tool names and descriptions. Additionally, poorly designed tools may return excessively large responses, cluttering the agent’s context window and impacting reasoning efficiency.