A Claude Skill is a reusable instruction module that teaches Claude how to reason and act in a specific context — think of it as a saved procedure. An MCP server is a piece of running infrastructure that gives Claude access to external data or systems — think of it as a connector. The distinction matters because ops leaders evaluating AI tooling regularly conflate the two, and buying the wrong layer wastes implementation budget on plumbing when you needed a procedure (or vice versa).
What it is NOT
A Claude Skill is not a software service. It does not run independently, it does not store data, and it cannot call external APIs on its own. It is a Markdown-based instruction file (or folder of files) that Claude loads and applies when the task matches. A Skill that helps your team review NDAs does not connect to your contract repository — an MCP server or another integration does that. If you only have a Skill and no connector, Claude reasons well but works only on what you paste into the conversation.
An MCP server is not intelligence. It does not know what to do with the data it surfaces. If you connect a CRM’s MCP server to Claude, Claude can now read your contact records and pipeline — but without a Skill or a prompt that encodes your team’s qualification logic, Claude will give generic answers about that data.
What each one does
Claude Skill: a folder containing instructions, reference files, and optionally scripts. Stored inside your Claude Code installation or shared via a plugin. Uses roughly 30–50 tokens of context when idle; the full instruction set loads only when Claude determines the Skill is relevant to the current task. A Skill encodes how Claude should handle a task — the steps, the thresholds, the output format, the failure modes to watch for. Example: a contract-redline Skill that carries your fallback clause library and your negotiation rules.
MCP server: a running process (local or hosted) that implements the Model Context Protocol. It exposes three categories of capability to Claude: resources (read-only data like files, database rows, calendar events), tools (functions Claude can call — create a record, send a message, run a query), and prompts (pre-built workflow templates). The server speaks JSON-RPC 2.0 over a transport layer (stdio for local, HTTP/SSE for hosted). Claude discovers available tools from the server’s manifest and calls them when they match the task. Example: a HubSpot MCP server that lets Claude read deal stages and update contact properties directly.
How they compose in ops workflows
Neither layer is complete on its own. The pattern that produces durable ops workflows pairs them:
- MCP handles the data boundary. Your HubSpot, your Salesforce, your contract repository, your ATS — each lives behind an MCP server (vendor-provided or self-hosted). Claude can read from and write to those systems through the server.
- A Skill handles the reasoning layer. The Skill tells Claude how to interpret what it pulls from those systems, which thresholds matter, which output format the team expects, and when to escalate rather than act.
A concrete RevOps example: a deal-review Skill instructs Claude to pull MEDDICC fields from Salesforce (via MCP), flag any gap in economic buyer or decision criteria, and return a structured briefing. The Skill is the logic; the MCP server is the pipeline into Salesforce. Swap out the CRM and you keep the Skill; update your qualification methodology and you keep the MCP server.
A legal-ops example: a contract-risk Skill carries fallback positions and risk thresholds for your standard vendor MSA. An MCP server connected to your CLM system fetches the live contract. Together, they let Claude produce a risk summary against your actual playbook, not a generic template.
When to build which
| Situation | What you need |
|---|---|
| Claude needs to read from or write to an external system | MCP server (or use a vendor-provided one) |
| Claude gives good answers when you paste data, but the work is repetitive | Skill — encode the procedure once |
| You want repeatable reasoning across your team without re-explaining context every session | Skill |
| A vendor says “we support Claude” and means they built a connector | MCP server — evaluate it using the questions in MCP server explained |
| You have a long system prompt you paste into every session | Convert it to a Skill — saves context and persists across conversations |
Token economics and why it matters
A five-server MCP setup with 58 combined tools consumes roughly 55,000 tokens of context window before any conversation starts (source: Anthropic tool-use documentation; Anthropic’s Tool Search feature reduces this by ~85% by deferring tool discovery until needed). A Skill loads 30–50 tokens at idle, then ~5,000 tokens when it activates. If you are running multi-step agentic workflows where context depth matters — pipeline reviews, contract analysis across many documents, sourcing sequences — the architectural choice affects throughput and cost directly.
The practical rule: lean on Skills for reasoning patterns you’ve already figured out. Lean on MCP servers for live data access. When you need both, connect them through a plugin or a Claude Code configuration that registers both the server and the Skill folder.
Common pitfalls
- Shipping an MCP server when a Skill would do. If the task doesn’t require live external data — scoring a pasted resume, reviewing a pasted contract clause, generating an outbound sequence — a Skill alone is faster to build and cheaper to run. Many teams over-engineer the data layer before they’ve codified any reasoning layer.
- Shipping a Skill with no data access and wondering why it can’t pull live records. A Skill cannot call your CRM. If the workflow requires current pipeline data, you need an MCP server or another integration feeding Claude the data.
- Evaluating MCP servers only on whether they connect, not on what they expose. A vendor can claim “MCP support” and expose only three read-only endpoints. Before relying on an MCP server in a workflow, check its tool manifest: which tools exist, whether writes are scoped, and whether it handles token refresh and rate limiting in production.
Related
- MCP server explained — a full primer on what MCP is and how to evaluate vendor MCP claims
- Prompt pack vs Claude Skill — when a saved prompt library is enough vs when to build a Skill
- Claude — the tool that runs both layers