How Claude Skills and MCP Servers Work Together

When engineers first wire Claude up to external systems, they tend to conflate two things that are actually doing very different jobs. They throw a pile of tool definitions at the model, stuff a long instruction block into the system prompt, and hope the model figures out when to call what. It works for a demo and falls apart the moment the surface area grows. The clean answer in the Claude ecosystem is a separation of concerns: MCP servers expose capabilities, and Agent Skills teach Claude when and how to use them. Understanding how those two layers fit together — from cold start to a returned answer — is the difference between an agent that scales and one that collapses under its own context.

This post walks the full architecture end to end. We will trace exactly what happens between a user typing a request and Claude producing a grounded answer, where each component lives, and why the boundaries are drawn the way they are.

The two layers: capability versus competence

Model Context Protocol is an open standard, introduced in November 2024, that connects Claude to external tools and data through MCP servers using a uniform client-server contract. An MCP server is a process — local over stdio or remote over HTTP — that advertises a typed catalog of tools, resources, and prompts. It is pure capability: a Postgres MCP server can run queries; a GitHub server can open pull requests. It knows nothing about your business logic or when those actions are appropriate.

An Agent Skill is the competence layer. A Skill is a folder of instructions, optional scripts, and reference files that Claude loads dynamically when a task looks relevant. The folder's entry point carries a short name and description that Claude reads cheaply; the heavier body only enters context once the Skill is actually triggered. So MCP answers "what can be done," and Skills answer "for this kind of work, here is the procedure, the gotchas, and which of those tools to reach for." Keeping them separate means you can swap a server implementation without rewriting guidance, and refine guidance without redeploying a server.

Walking a single request end to end

Consider an engineer in Claude Code asking, "Reconcile yesterday's failed payments against the ledger and open a ticket for each mismatch." Several subsystems light up in sequence. The model first checks which Skills are even relevant by scanning the lightweight Skill metadata already in context. A "payments-reconciliation" Skill matches, so its full instruction body is loaded. That body tells Claude the canonical reconciliation steps and names the relevant MCP tools — a payments server, a ledger database server, and an issue-tracker server. Only then does Claude begin issuing tool calls.

flowchart TD
  A["User request in Claude Code"] --> B{"Relevant Skill?"}
  B -->|No match| C["Claude answers from base context"]
  B -->|Match| D["Load Skill instructions & scripts"]
  D --> E["Skill names which MCP tools to use"]
  E --> F["Claude calls MCP server (typed tool)"]
  F --> G["Server executes & returns structured result"]
  G --> H{"More steps in Skill?"}
  H -->|Yes| F
  H -->|No| I["Claude composes grounded answer"]

The loop in the middle is the heart of the architecture. Claude does not blast all three servers at once; it reads the Skill's procedure, calls the payments server to fetch failures, feeds those rows into a ledger query, compares, and only then opens tickets. Each tool result returns as structured JSON the model can reason over, not free text it has to parse. That structure is what lets a multi-step agentic task stay reliable across a dozen calls.

Where each piece actually runs

It helps to be concrete about process boundaries. The Claude model runs on Anthropic's side. The MCP client lives inside the host — Claude Code, Claude Cowork, or an app built on the Claude Agent SDK. That client is what maintains connections to servers, performs the initial capability handshake, and marshals tool-call requests from the model into actual server invocations. The MCP servers themselves are yours: a local stdio process started by Claude Code, or a remote HTTPS endpoint you operate. Skills are files on disk (or bundled into a Cowork plugin) that the host surfaces to the model.

This topology has a practical consequence: secrets and side effects stay on your infrastructure. The model never holds your database password; it asks the MCP client to ask the server, and the server holds the credential. When people worry about handing an agent the keys to production, this boundary is the answer — Claude reasons about what to do while the server controls whether it is allowed.

Progressive disclosure keeps context cheap

The non-obvious genius of the design is progressive disclosure of context. If every Skill's full text and every server's complete tool schema were always loaded, a moderately equipped agent would burn tens of thousands of tokens before reading the user's request. Instead, Skills expose only a name and one-line description until invoked, and MCP tool schemas are summarized so the model knows a tool exists and roughly what it does without ingesting every nested parameter description up front.

That layering is why a single agent can have access to fifty Skills and a dozen servers without drowning. The architecture treats context as the scarce resource it is. When you design your own Skills, you are really designing a disclosure ladder: cheap trigger metadata at the top, the procedure in the middle, and bulky reference material in files the Skill can pull in only if a specific branch demands it.

Failure handling lives at the seams

End-to-end reliability is decided at the boundaries between layers. When an MCP server returns an error — a 429, a constraint violation, a timeout — that result flows back to Claude as a structured tool error, and the Skill's instructions ideally tell the model how to respond: retry with backoff, fall back to a read-only path, or surface a clear message. A server that returns a raw stack trace forces the model to guess; a server that returns a typed error code with a human-readable hint lets the Skill's guidance kick in deterministically. The architecture rewards servers that fail in well-shaped ways.

Frequently asked questions

Do I need both Skills and MCP, or can I use one alone?

You can use either independently. A Skill with no MCP tools is just procedural knowledge; an MCP server with no Skill works if the model can infer usage from tool descriptions. But the pairing is where the leverage is: MCP supplies the verbs, Skills supply the judgment about when and how to use them. For anything beyond a couple of tools, the Skill layer is what keeps tool selection accurate.

How does Claude decide which Skill to load?

Claude reads the lightweight name and description metadata of available Skills against the current task. When a description clearly matches the work at hand, the full Skill body is pulled into context. This is why a sharp, specific Skill description matters more than a clever one — it is the matching signal.

Are MCP servers always remote services?

No. Many run locally over stdio — a script Claude Code launches on your machine — which is ideal for filesystem access or local databases. Remote servers over HTTP are used for shared, multi-user, or hosted capabilities. The model and Skill layer behave identically either way; only the transport differs.

Bringing agentic AI to your phone lines

CallSphere takes this same layered architecture — capabilities exposed through tools, judgment supplied by skill-like guidance — and points it at voice and chat, so AI agents answer every call and message, pull data mid-conversation, and book real work around the clock. See it live at callsphere.ai.

Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.