When to Use a Claude Skill Versus an MCP Server: The Complete Decision Guide

Use a Claude skill for high-level workflow orchestration and instruction packaging, while an MCP server exposes executable tools and handles authentication for external APIs.

The ComposioHQ/awesome-claude-skills repository defines two distinct but complementary approaches to extending Claude's capabilities. Understanding when to use a Claude skill versus an MCP server is critical for building efficient AI-driven workflows that balance high-level orchestration with raw execution power.

What Is a Claude Skill?

According to the source code in README.md (lines 101-106), a Claude skill is a reusable instruction package that tells the agent what to do and how to do it.

Skills are defined by a folder containing a SKILL.md file with YAML frontmatter specifying the name and description, followed by the full instructional body in markdown. The agent initially sees only the skill's name and short description—approximately 100 tokens—and loads the remaining content on demand.

Critically, skills do not expose tools or handle authentication themselves. Instead, they orchestrate a series of actions, embed guardrails, and may ship auxiliary scripts or reference assets.

What Is an MCP Server?

An MCP server (Model Context Protocol server) is a lightweight service that exposes tools—executable functions—to the LLM. As documented in mcp-builder/reference/python_mcp_server.md (lines 47-50), MCP servers handle authentication, transport, and tool discovery for external APIs or services.

Tools are the primitive operations the LLM calls, such as search_web or create_issue. MCP servers can be built in Python using FastMCP or in TypeScript using official SDKs, following the naming convention {service}_mcp.

When to Use a Claude Skill

Choose a Claude skill when you need to package instructions and orchestrate existing capabilities rather than building new infrastructure:

• Workflow orchestration: You need to describe a multi-step process (e.g., "read a PDF → extract tables → generate a summary"). Skills let you bundle the entire process with progressive loading for efficiency.

• Existing tools available: The required actions are already available as existing tools (e.g., the connect-apps plugin) and you only need higher-level orchestration. The skill can call those tools without writing new server code.

• Guardrails and templates: You want to ship prompts, guardrails, or templates that guide the LLM's reasoning. The markdown body of a skill is ideal for embedding detailed instructions and examples.

• File-centric distribution: You prefer version-controlled, easy-to-review distributions. Skills are simply markdown plus optional scripts, making pull requests straightforward.

When to Use an MCP Server

Build an MCP server when you need to expose new capabilities or handle secure integrations that require centralized management:

• Custom API endpoints: You must expose custom API endpoints or proprietary services that have no existing tool. Implement a tool in the server and let the LLM invoke it via the MCP protocol.

• Secure authentication: The integration requires OAuth or API keys that should be handled centrally. MCP servers manage auth and can enforce team-based access controls.

• High-performance calls: You need low-latency calls with streaming Server-Sent Events (SSE) or binary payloads. MCP supports multiple transports (stdio, SSE, HTTP) optimized for specific use cases.

• Reusable toolsets: You want to share a catalog of tools across many skills or agents. An MCP server exposes tools that multiple skills can discover dynamically.

• Domain-specific services: You are building a service (e.g., ticket system, finance API) that evolves independently of the skill layer. Separate the service (MCP) from the orchestrator (Skill) to enable independent versioning.

Production Architecture: Combining Both Layers

In practice, production agents typically combine both layers. The MCP server supplies raw capabilities (tools), while one or more Claude skills coordinate those tools into end-to-end user-visible behavior.

For example, a skill might handle the conversation flow and decision logic while delegating specific actions—like sending emails or creating GitHub issues—to an MCP server that manages the API authentication and transport layers.

Implementation Examples

Minimal Claude Skill (Connect-Apps Plugin)

The connect-apps plugin demonstrates a skill that orchestrates existing MCP tools without implementing server code. Located at connect-apps-plugin/SKILL.md, it defines:

---
name: "Connect Apps"
description: "Send emails, create GitHub issues, post to Slack using Composio."
---

Claude can invoke the following tools provided by the MCP gateway:
- `email_send`
- `github_create_issue`
- `slack_post_message`

When the user asks to "schedule a meeting and notify the team", the skill:
1. Calls `calendar_create_event`.
2. Calls `slack_post_message` with the event link.
3. Returns a concise confirmation.

This skill relies on the MCP gateway already exposing the needed tools; no server implementation is required.

Python MCP Server with FastMCP

To expose new functionality, implement an MCP server using FastMCP. As shown in mcp-builder/reference/python_mcp_server.md (lines 47-50), follow the {service}_mcp naming convention:

# server.py

from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field, ConfigDict

mcp = FastMCP("github_mcp")

class CreateIssueInput(BaseModel):
    model_config = ConfigDict(extra="forbid")
    repository: str = Field(..., description="owner/repo")
    title: str = Field(..., min_length=1, max_length=100)
    body: str = Field("", description="Issue body text")

@mcp.tool(name="github_create_issue", annotations={"title": "Create GitHub Issue"})
async def github_create_issue(params: CreateIssueInput) -> str:
    """Creates an issue via the GitHub REST API and returns the URL."""
    # Implementation details omitted for brevity

    return f"https://github.com/{params.repository}/issues/123"

This server registers the github_create_issue tool. A Claude skill can now reference this tool to automate issue creation workflows.

Summary

• Claude skills package instructions and orchestrate workflows using existing tools, loading efficiently via progressive disclosure (~100 tokens initially).
• MCP servers expose new tools, handle authentication, and manage transport layers for external APIs.
• Use skills for high-level coordination, guardrails, and when working with existing tool sets.
• Use MCP servers for custom API integrations, centralized auth, and performance-critical operations.
• Production systems combine both: MCP servers provide capabilities, while skills provide orchestration.

Frequently Asked Questions

Can a Claude skill expose custom tools?

No. According to the repository's README.md (lines 105-106), Claude skills do not expose tools or handle authentication themselves. They only orchestrate existing tools. To expose new functionality, you must implement an MCP server.

Do MCP servers support streaming responses?

Yes. MCP servers support multiple transport protocols including stdio, Server-Sent Events (SSE), and HTTP. This architecture supports high-performance, low-latency calls including streaming responses and binary payloads, making them suitable for real-time applications.

How do I distribute a Claude skill to my team?

Skills follow a file-centric distribution model. Since a skill is simply a directory containing a SKILL.md file with YAML frontmatter and optional auxiliary scripts, you can version control it through standard Git workflows. Team members can review changes via pull requests and import the skill by referencing the file path or repository URL.

Should I build an MCP server or a Claude skill first?

Build an MCP server first if you need to integrate with a new API or service that lacks existing tool support. Once the tools are available via MCP, create a Claude skill to orchestrate those tools into specific workflows. This separation allows the service layer (MCP) to evolve independently from the orchestration logic (skill).

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}