Intermediate7 min read

MCP + Agents, IDEs, and Products

MCP does not replace agents or IDEs — those apps are hosts that plug into servers; the agent loop still decides, and a product can be both a host (users chat in it) and a server (other hosts call it).

Explain like I'm new to AI

Three places you'll meet MCP:

IDEs

Cursor, VS Code, JetBrains

The editor is the host. MCP servers become project tools: git, docs, tickets, browsers.

Agents

Claude, ChatGPT, custom loops

The agent loop calls MCP tools the same way it calls any function — MCP is the catalog, not the loop.

Product surfaces

Slack, Figma, internal apps

A product can be a host (users chat inside it) or a server (other hosts call into it).

MCP is the plug. The host (IDE, agent, or product) decides when to call; the server just exposes capabilities.

IDEs (Cursor, VS Code, JetBrains) — the editor is the host. Servers become project-aware tools: git, docs, tickets, browsers.
Agents (Claude, ChatGPT, your loop) — observe → reason → plan → act, where "act" is often an MCP tools/call.
Products (Slack, Figma, Linear, your SaaS) — users might chat inside the product (it's a host), and the product also publishes a server so Cursor can file a Linear issue.

Same protocol, different UX.

Mental model

MCP is electrical outlets. The oven (agent), the lamp (IDE), and the workshop (Slack app) all use the same outlet shape. The outlet doesn't cook dinner. The agent still has to plan; the IDE still has to show diffs; the product still has to enforce permissions.

How it works

Inside an agent: after tools/list, MCP tools look like ordinary function-calling tools. The loop doesn't change. What changes is where tools come from — vendors and internal teams, not only code you wrote.

Inside an IDE: extra UX — a server list, a prompt picker (slash commands), resource attachments (open this file as context), and approval for writes. Many IDE actions are still non-agent (you clicked "run prompt").

Inside a product: you may implement both sides: a host for in-app assistants, a server for the ecosystem. The MCP Apps extension covers richer in-context UI; Tasks covers long-running jobs agents shouldn't block on.

Real-world example

A designer generates UI, sends it into Figma via Figma's MCP server, then a developer in Cursor (host) uses the same company's Linear server to file implementation tickets. Nobody rebuilt Figma or Linear integrations for each app. The agent in Cursor still chose when to file the ticket.

Technical explanation

Clear boundaries prevent confused architectures:

Piece	Owns
Agent loop	When to call, when to stop, memory, retries
Host policy	Which servers, which user, HITL, logging
MCP	Catalog + invocation + (remote) auth
Server	Domain actions and data, least privilege

Multi-agent systems can share MCP servers (a researcher and a writer both read the handbook server) or isolate them (only the finance agent gets the payments server). Isolation is a host decision.

Don't hide a whole agent inside an MCP tool unless you must — nested agents behind a single tools/call are hard to debug, authorize, and evaluate. Prefer the host's loop, with MCP as the hands.

Common mistakes

Common mistake

Rewriting your agent framework "in MCP." MCP has no planner and (as of 2026) no protocol session. Planning, memory, and stopping conditions stay in the host.

Giving every agent in a multi-agent system every server. Least privilege still applies across agents.
Assuming IDE = always an agent. Many MCP uses in editors are human-picked prompts and resources.

When to use it

Whenever an agent or product should use shared, vendor-maintained, or cross-app tools.

When NOT to use it

Tight, single-app functions with no ecosystem story — extra protocol without payoff.

Alternatives

Native IDE extensions; in-app tools registered only in that agent runtime.

Quick quiz

Related concepts

Consuming an MCP Server — A host consumes MCP by connecting a client per server, mapping the catalog into function calling, and enforcing allow-lists and approvals.
The Agent Loop — The agent loop is the cycle of observe, reason, plan, and act that repeats — using tools and results — until the agent reaches its goal.
Tool Selection — Tool selection is how an agent picks the right tool for a step by matching the task to each tool's name and description — so good tool design is critical.