When to Use MCP vs. Skills with Claude: A Practical Decision Framework

Background

If you are new to the field, MCP (Model Context Protocol) acts as a universal adapter that lets Claude connect in real time to external systems such as JIRA, GitHub, or databases, effectively giving the model a "hand" to fetch live data.

Skills, on the other hand, are Markdown files that encode "how to do something" – they describe team workflows, code‑review standards, or repeatable reporting processes, giving Claude a "brain" for specific scenarios without real‑time data fetching.

In short, MCP knows what is happening now; Skills know how things should be done.

Two Types of AI Engineers

MCP‑first advocates believe MCP is the future, a universal adapter that solves N×M integration problems, and argue that if Claude can reach directly into your tools, writing scripts is unnecessary.

Skills‑only proponents argue that MCP is heavyweight, consumes tokens, and is overkill for 90% of tasks. Skills are lighter, faster, and can be written in plain English; a well‑crafted Skill can handle the work without real‑time connections.

Both sides have valid points, but insisting on one extreme can cause you to miss the most efficient solution.

Weighing Pros and Cons

MCP is powerful and easy to start with but can be token‑hungry for simple, repetitive tasks. Skills combined with APIs are lightweight and excel when the workflow is well‑defined, though they require more setup and rely on stable APIs.

Which Should You Use?

The key insight is that LLMs like Claude can generate code/scripts. Recognizing that a Skill that knows how to call an API can be as powerful as MCP leads to a decision framework called the "Three‑Question Test":

Is the process clear and repeatable? Can you write down the steps in a predictable, linear flow?

Does it need external data? (JIRA, GitHub, Slack, databases, etc.)

Does the service provide a well‑documented API? Not just any API, but one that supports the required operations.

If the answer to all three is "yes," you have the conditions to build a Skill; otherwise, MCP is likely the better choice.

High‑Level Architecture Diagrams

Two Real‑World Cases

Case 1: Cycle‑time Report (JIRA + GitHub)

Goal: generate a report linking JIRA stories to their GitHub PRs, showing the full lifecycle from ticket to merge. This fits a Skills + API approach because:

The steps are well‑defined and stable across sprints.

Both JIRA and GitHub provide reliable APIs.

The process needs to be fast and consistent.

Solution: build a Skill that directly calls the APIs and produces the report.

Case 2: PRD Coverage Check

Scenario: a new PRD is submitted and the team asks whether the current Epic and its Stories cover the required content. This is an MCP use‑case because:

No clear script exists; Claude must reason about coverage rather than just fetch data.

The task is likely one‑off, not a repeatable sprint activity.

The value lies in Claude's judgment, not in repeatable execution.

Solution: let MCP handle the heavy lifting and direct Claude to the relevant JIRA data.

The Truth About Complexity

MCP is easier to adopt—plug it in and it works. Skills + API require more setup, especially handling authentication, error handling, and rate limits for external APIs.

Practical advice: start with MCP to explore. As patterns emerge (e.g., "I do this every sprint" or "Fetching 200 records one by one is too slow"), those signals indicate it’s time to upgrade to Skills + API.

Advanced Operation: Wrap in a Slash Command

Regardless of whether you use MCP or Skills + API, expose the workflow as a slash command for consistent user interaction: /cycle_time_report This abstraction lets you swap the underlying engine (MCP today, Skills + API tomorrow) without users noticing.

Remapping Our Architecture Diagram

Start with the MCP approach:

Once you understand the details, transition to Skills + API without breaking the end‑user interface: