MCP vs Tools vs Skills: The Extension Taxonomy

The rapid evolution of AI agents has introduced a confusing vocabulary regarding how Large Language Models interact with the outside world. Developers often face a choice between implementing the Model Context Protocol, defining custom tools, or building internal skills for their agents. Understanding the MCP vs Tools vs Skills taxonomy is essential for building scalable AI workflows that do not collapse under the weight of fragmented abstractions. This post breaks down these three concepts to help you decide which integration strategy fits your specific project requirements.

Defining the Taxonomy

To build effective AI systems, we must distinguish between the protocol, the interface, and the capability. While these terms are sometimes used interchangeably in casual conversation, they represent different layers of the agentic stack.

Model Context Protocol (MCP)

The Model Context Protocol is an open standard designed to enable seamless integration between AI applications and data sources. Instead of writing a custom connector for every service (such as Google Drive, Slack, or a local database), MCP provides a unified way for a server to expose resources, prompts, and tools to a client.

An MCP implementation typically involves a host, a client, and a server. The host is the primary application the developer interacts with. The server provides the specific data or functionality. Because it is a standardized protocol, any client that supports MCP can theoretically connect to any MCP server without additional configuration.

Tools

In the context of LLMs, a tool is a specific function that a model can decide to call based on its training and the system prompt. Tool use, also known as function calling, requires the developer to provide a JSON schema describing the function parameters. The model does not execute the code. Instead, it outputs a structured request, the application executes the logic, and the result is fed back to the model.

Tools are generally atomic and scoped to a specific task. Examples include:

• Generating a file system tree.
• Querying a specific API endpoint.
• Executing a calculated math expression.

Skills

Skills represent a higher level of abstraction than simple tools. A skill often involves a sequence of steps, a predefined strategy, or a specialized set of instructions that guide the model on how to achieve a complex goal. While a tool might be "write to file," a skill would be "refactor this React component to use hooks." Skills combine domain knowledge with tool usage to deliver a specific outcome.

Technical Comparison of Integration Patterns

Choosing the right approach depends on whether you value standard compatibility, granular control, or high level abstraction.

When to Use MCP

MCP is best suited for scenarios where you need to connect to established data silos or common developer utilities. If you are building a workspace where you want to pull in data from GitHub, Jira, and your local file system, using existing MCP servers is more efficient than building custom integrations. The standardized nature of local-first data handling in many modern tools makes MCP a strong candidate for privacy conscious environments.

When to Use Tools

Native tools are the right choice when you need the lowest possible overhead and absolute control over the execution environment. If you are building a CLI tool that needs to perform a very specific, proprietary operation, defining a tool schema directly in your agent logic is often simpler than setting up an MCP server architecture.

When to Use Skills

Skills are necessary when the logic is too complex for a single function call. When an agent needs to "understand" a codebase or follow a specific architectural pattern, you are building a skill. In Ask Deska, the ability to drive the workspace by opening panels and checking sessions is a combination of built-in tools and the skill of spatial management.

Practical Implementation in Development Environments

Modern developer tools are beginning to aggregate these three categories into unified workspaces. For example, a developer might use an infinite canvas to organize their thought process. In such a setup, the UI must handle the output of various agents simultaneously.

When using coding agents, the choice of taxonomy becomes practical. An agent like Claude Code or Codex CLI might use tools to read your local files. If you run these agents side-by-side in a terminal, they are essentially using tools to interact with your operating system.

Data Privacy and Local Execution

A significant consideration in the MCP vs Tools vs Skills debate is where the data lives. Tools that rely on cloud-based function calling may leak sensitive context. A local-first approach ensures that even when an agent uses a tool to read a file, that file content stays on your machine. This is a core philosophy for tools that prioritize privacy.

Managing Complexity with Deska

Deska acts as a host environment for these diverse agent capabilities. Instead of forcing you to choose one specific method, it provides a flexible workspace where different types of integrations can coexist.

In the Deska workspace, you can place multiple terminals and code editors on an infinite canvas. This allows you to run a specialized agent using a specific toolset in one panel, while another panel handles a different task. The Ask Deska assistant serves as a bridge, using its own skills to manage the layout of these panels based on your voice or chat commands.

For developers who need to step away from their machine, the mobile app provides a way to monitor these agent activities through a secure relay. This ensures that whether your agent is using an MCP server to fetch data or a local tool to run a test suite, you maintain visibility without exposing your local ports to the public internet.

Summary of Use Cases

1. Use MCP when you want to leverage a community-maintained library of data connectors.
2. Use Tools when you need high-performance, single-purpose actions within a specific LLM's ecosystem.
3. Use Skills when you are defining complex behaviors, workflows, or specialized domain expertise.

The landscape is moving toward a hybrid model. Professional developer environments will likely support all three, allowing developers to pick the most efficient abstraction for each part of their pipeline.

FAQ

Is MCP better than function calling?

It is not necessarily better, but it serves a different purpose. Function calling is a feature of a specific model, while MCP is a protocol that allows many models to talk to many different data sources using a single standard. MCP often uses function calling under the hood to execute its tasks.

Can I use MCP servers with any AI agent?

You can use them with any agent or host application that has implemented the MCP client specification. If an agent does not natively support MCP, you would need to write a wrapper to convert the MCP server outputs into a format the agent understands.

Do I need to pay for MCP integrations?

The protocol itself is free and open source. However, the specific services you connect to might have their own costs. For instance, using an MCP server for a paid API will still require your own credentials. In Deska, the pricing model is based on whether you bring your own keys or use managed inference, but the workspace and its ability to host these agent interactions remain accessible.

Download Deska for Your Workflow

If you want to experience an environment where agents, tools, and code come together on an infinite canvas, you can download Deska for Mac, Windows, and Linux. It provides the infrastructure to run multiple coding agents side by side, giving you the freedom to organize your developer tools exactly how you see fit.