MCP vs Tools: What Is the Difference?

A tool is a function the model can call. You describe it with a JSON schema, the model emits a structured call, your code runs it. MCP (the Model Context Protocol) is the layer above that: a protocol for serving those tool definitions from a separate process, so any MCP-capable client can discover and call them without your code knowing they exist. They are not competing choices. Every MCP tool call still ends as an ordinary function call in some process.

The two layers, side by side

The confusion comes from the word "tool" doing double duty. In an SDK it means one entry in a tools array. In MCP marketing it means a whole server full of them. They describe different jobs.

	Tool / function calling	MCP
What it is	A calling convention between model and host	A transport and discovery protocol (JSON-RPC)
Who defines the schema	Your application code	An MCP server, fetched at connect time
Where the implementation runs	In your process	In the server's process, under its own credentials
What the model sees	A JSON schema and a name	A JSON schema and a name (identical)
Added moving parts	None	A server process, a connection, a lifecycle
Reuse across apps	Copy the code	Point another client at the same server

The last two rows are the whole decision. The model cannot tell the difference: a schema that arrived over JSON-RPC and a schema you typed into an array look the same by the time it plans a call. MCP changes who owns the definition, not how the call is made.

When a plain tool is enough

If one application calls a fixed set of functions against one model, defining them in code is the smaller system. You get types from your own language, a stack trace when something throws, and no second process to supervise or version. A search_orders function that reads your own database has no reason to travel over a protocol to reach code sitting in the same repo.

Three signs you are still in this case:

1. The tools only make sense inside one product.
2. You ship the tools and the agent together, in one deploy.
3. Nobody outside your team wants to call them.

Reaching for MCP here buys a connection lifecycle, a schema negotiation, and a new failure mode, in exchange for reuse you are not going to use.

When MCP earns the extra process

MCP solves an integration-count problem. Wire M models or clients to N tool sets by hand and you write M × N adapters. With MCP each client implements one protocol and each tool set implements one server, so the same work is M + N. Five clients and ten tool sets go from fifty integrations to fifteen.

That arithmetic only pays when the multiplication is real. It usually is when:

• The same capability serves several clients. A Search Console connector that Claude Desktop, a coding agent, and an internal bot all want is one server instead of three integrations.
• The tool belongs to someone else. A vendor can ship a server you connect to, without you writing an adapter for their API and without them writing one for your framework.
• The credentials should not live in your app. The server runs under its own auth, so your process never holds the token. That boundary is often the real reason to split, not the reuse.
• The tool set changes independently of the app. Servers can add tools that existing clients discover at connect time, with no client release.

Where Skills fit, and why they are a third thing

Developers building on coding agents hit a third term and assume it competes with the other two. It does not. A Skill is instructions: a document that teaches an agent a procedure, the order of operations, what "done" looks like. It has no schema and no runtime.

The three answer different questions:

• Skill answers how should this work be done.
• Tool answers what can be called.
• MCP answers where the callable things come from.

A Skill with no tools produces good advice and no actions. Tools with no Skill produce an agent that can call twelve functions and no plan for which. Most working setups pair a Skill for the procedure with tools for the data, and reach for MCP only when those tools need to be shared. For the SEO version of that split, we compared skills and MCP servers head to head.

A worked example: SEO inside a coding agent

SEOAgent is built on the Skill-plus-CLI side of this line rather than as an MCP server, and the reasoning generalizes.

The work is a procedure: crawl the live origin, check each page against an audit protocol, write the fix into the file that renders the page, show the diff. Most of that is reading and writing files in a repo the agent already has open. A coding agent can run seoagent crawl in the terminal it already has, so the CLI is the tool surface and the Skill is the procedure. No server, no connection to keep alive, and the audit artifacts land in .seoagent/ as files you can read in a diff.

The honest cost: an agent without shell access cannot use it, and the plain chat apps that speak MCP cannot either. That is the trade. Where the data genuinely is remote and shared, an MCP server is the better shape, which is why a Search Console MCP pairs well with a Skill rather than replacing it.

If you want this inside your own agent, the setup is agent-specific: Claude Code, Cursor, and Codex each get the same Skill through a different discovery file.

How to decide in one pass

Ask where the definition should live.

• Same repo, same deploy, one consumer → define the tool in code.
• Different owner, several consumers, or credentials you want out of your process → put it behind an MCP server.
• Nobody knows what the agent should do with either → the gap is a Skill, and adding more tools will not close it.

Frequently asked questions

Is MCP a replacement for function calling?

No. MCP delivers tool definitions to a client; function calling is how the model requests one of them. An MCP tool invocation still terminates in a normal function call inside the server process. Choosing MCP means choosing where schemas live and who runs the implementation.

Is MCP slower than a local tool?

It adds a process boundary and a round trip, so yes, by the cost of that hop. In agent workloads the hop is usually small next to the model's own latency. The cost worth watching is different: every tool a server exposes takes context-window space in the client, so a server with forty tools taxes every request whether or not the agent uses them.

Do I need MCP to use tools with Claude or GPT?

No. Both support tool definitions passed directly in the request. MCP is useful when you want the same tools available in clients you did not write, such as a desktop app or someone else's agent.

What is the difference between an MCP server and a Skill?

An MCP server supplies capability: callable functions with schemas, running in their own process. A Skill supplies procedure: written instructions that tell the agent what to do and in what order. They are complementary, and a setup missing either one shows it. An agent with servers but no Skill improvises; an agent with a Skill but no tools writes plans it cannot execute.

Can one agent use both?

Yes, and most do. A coding agent typically has built-in file and shell tools, a Skill or two for procedures, and MCP servers for data that lives outside the repo.

References

• Model Context Protocol specification — the protocol, its transports, and the server lifecycle.
• Best SEO tools for Claude Code — the skill-versus-MCP split applied to one domain.
• The AI SEO workflow for coding agents — what the procedure layer looks like end to end.