Agent Skills vs MCP Tools: Which Should You Use?

With the release of Agent Skills and the Model Context Protocol (MCP), developers now have two powerful ways to extend Agent's capabilities. But which one should you choose for your project?

This guide breaks down the differences, strengths, and use cases for each to help you make the right architectural decision.

Quick Comparison Table

Feature	Agent Skills	MCP Tools
Primary Goal	Specialization & behavior	Data access & discrete actions
Structure	Markdown + Scripts	JSON-RPC Protocol
Deployment	Local files / Git repo	Local or Remote Server
Complexity	Low (Prompt-centric)	Medium (Code-centric)
Best For	Workflows, expert personas	Database connections, API wrappers

What are Agent Skills?

Agent Skills are primarily about teaching Claude "how" to think and act. They are packages of instructions (prompts) combined with simple scripts.

Strengths:

• Rapid Iteration: You can change behavior by just editing a markdown file.
• Persona adoption: Great for creating a "Senior Engineer" or "Legal expert" persona.
• Progressive Loading: Only loads instructions when needed, saving context.

Use Case: " The Report Writer"

You want an agent that takes raw data and writes a report in a specific company style. An Agent Skill is perfect here because it's mostly about instruction following and formatting.

What is MCP (Model Context Protocol)?

MCP is a standard for connecting Claude to external systems. It's an API specification that allows Claude to query databases, read repositories, or control interfaces.

Strengths:

• Universal Compatibility: Works across different AI clients (Claude Desktop, IDEs).
• Security: Runs in a separate process/container.
• Standardization: A growing ecosystem of pre-built servers (Postgres, Google Drive, Slack).

Use Case: "The Database Query Tool"

You want Claude to be able to run SQL queries against your production database. An MCP Server is the right choice. It exposes specific tools (query_db, list_tables) safely and reliably.

When to Use Both?

The most powerful agents often combine both!

Scenario: You want an agent to fix bugs in your repo.

1. MCP: Use the git and filesystem MCP servers to give Claude access to the files.
2. Agent Skill: Create a bug-fixer Skill that contains instructions on your coding standards, error handling patterns, and test requirements.

Detailed workflow:

• The Skill guides the high-level reasoning ("First check the tests, then isolate the bug").
• The MCP tools execute the low-level actions ("Read file x", "Run test y").

Decision Framework

Ask yourself these questions:

1. Is this about "knowledge/process" or "capability"?

   • Knowledge/Process → Agent Skill
   • Capability/Access → MCP

2. Do I need to connect to a 3rd party API?

   • Yes → MCP is usually better (cleaner separation of concerns).

3. Do I just need to enforce a specific output format?

   • Yes → Agent Skill (just prompt engineering).

Conclusion

Think of MCP as the "Hands" (tools, strength, reach) and Agent Skills as the "Training" (technique, strategy, specialized knowledge).

By leveraging both, you can build agents that are not only capable of doing things but doing them expertly.