Quick Answer
Skills tell an AI how to do a job. MCP, the Model Context Protocol, connects an AI to the tools and data it needs to do that job. You don't choose between them: a skill can direct the AI to use tools reached through MCP, and MCP can be the route that delivers skills to the AI.
What Skills and MCP Are
A skill is a saved set of instructions for one job, usually a folder with a SKILL.md file. It holds the steps, the output format and the checks (What Are Agent Skills?). Think of a new starter's job card: it says what good work looks like, but it doesn't open any doors.
MCP is an open protocol for connecting AI apps to outside systems. The AI app, called the host, opens a connection to each MCP server it uses. A server can offer tools (actions the AI can take, such as creating a record), resources (information the AI can read, such as a file or a database entry) and reusable templates (MCP architecture). In new-starter terms, MCP is the building pass and the logins: it decides which systems they can reach.
Anthropic sums up the split as MCP handling connectivity and skills handling expertise (Anthropic: Skills and MCP).
Skills vs MCP Side by Side
| Skill | MCP | |
|---|---|---|
| What it is | Written instructions for one job | A protocol for connecting AI apps to tools and data |
| The question it answers | How should this work be done? | What can the AI reach and act on? |
| What it's made of | A SKILL.md file, plus optional templates and scripts | A server offering tools, resources and templates |
| Who usually writes it | The person who knows the job | A developer or the software vendor |
| What goes wrong without it | Results vary and miss your format | The AI can't read the source or take the action |
| Example | A proposal writer | A CRM connector |
How They Work Together
A skill can use an MCP server. The skill names the tool and says how to use it: which records to pull, what to check and when to stop. Microsoft's Copilot Studio documentation describes the same pattern, where a skill tells an agent to use a specific tool in a particular way (Microsoft: Skills overview).
The AI vendors also package the two together. In ChatGPT, plugins combine skills with MCP servers for connected services (OpenAI: Skills and plugins). In Claude, plugins bundle skills, connectors and sub-agents (Anthropic: Use plugins in Claude).
MCP can also carry the skills themselves. That's how Contexta works: a connected AI reaches Contexta through MCP and fetches the published skills and company context it needs.
One caution. An MCP connection doesn't mean everything is reachable. What the AI can actually use depends on the server, the sign-in and permissions behind it, and what the AI app supports.
Worked Example: A Proposal for Harbour Foods
Fernhill Studio, a fictional design agency, needs a proposal for a prospective client, Harbour Foods (also fictional). A team member asks their AI: "Draft the proposal for Harbour Foods."
Here's what each part contributes:
- The proposal skill sets the sections (summary, scope, pricing, next steps), the pricing rules, the tone and a check that every price matches the current price list.
- A CRM connection over MCP supplies the deal record, the contact names and the stage.
- A document connection opens the meeting notes and the proposal template in SharePoint.
- Contexta, also over MCP, supplies the proposal skill and Fernhill's company context: its offer, its terms and how it writes.
When the draft comes back, the cause of any problem points to the fix. Wrong sections or an invented discount point to the skill. Missing client goals point to the connection to the meeting notes. Better instructions can't make up for a document the AI can't open, and a working connection can't fix an unclear method.
Try It: Map One Task to Skills and Connections
- Pick one task and write the request exactly as someone would ask it.
- List what the AI must know how to do. That's the skill.
- List what the AI must reach: documents, records, apps. Those are the connections.
- Check which connectors your AI tool already has and whether you're signed in to each one.
- Write or open the skill, and name the sources and tools it should use.
- Run the task and ask the AI to list the sources and tools it actually used.
- Fix each gap on the side it belongs to: the skill or the connection.
Test It: Three Cases
- A normal request. With every connection working, check that the client details match the CRM and the goals match the meeting notes.
- Incomplete input. Run it without access to the meeting notes. The skill should report the missing source and ask for it, not invent the client's goals.
- An unrelated request. Ask the AI to move the deal to "won" in the CRM. The proposal skill shouldn't take over, and any change to the CRM should wait for your approval.
Where Contexta Fits
A skill tells your AI what to do. Your knowledge base shows it how, in detail. Your company context tells it who it's for. The Contexta harness brings them together, in every AI.
Contexta uses MCP as its delivery route. Each organisation connects once, and people sign in with their work email. At the start of a session, a connected AI loads your organisation's rules, picks the right space and fetches the published skill for the job. Company context comes with every skill: your terminology, naming and formats, set for the company and adjusted for each team.
Your knowledge base stays in your own SharePoint or Google Drive. Contexta doesn't store or index your documents, so your AI needs its own access to them through its document connector. See What Is an AI Knowledge Base?. Spaces decide which home and rules apply to a piece of work; they don't change who can open which files. Every change to a skill or rule is kept in version history.
Claude, ChatGPT and Cursor are tested with Contexta. Check Integrations for supported setups, and see How It Works for the steps.