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MCP Explained

The Model Context Protocol is the USB-C of AI tooling. Here's what it is, why it matters, and how to think about it.

6 min readPublished Mar 2025Updated Apr 2026
MCPProtocolToolsAgents
Edited by The AIKnowHub team · Editorial team

Key takeaways

  • 1MCP is a standard — like USB-C — not a model, runtime, or framework.
  • 2It defines three primitives: resources (read), tools (act), and prompts (templates).
  • 3Servers usually run locally with your credentials, so private data stays on your machine.
  • 4The killer benefit: write GitHub-Slack-DB integrations once, use them across every AI client.
  • 5MCP is small enough to learn in an afternoon. Use the official TypeScript or Python SDK.

The pitch

Before MCP, every AI app integrated tools by hand: GitHub plugin for ChatGPT, GitHub plugin for Claude, GitHub plugin for Cursor, GitHub plugin for Continue. Every model, every tool, written N×M times.

Model Context Protocol (MCP) is an open standard that lets any AI client talk to any tool server through a single shared interface. Write the GitHub server once; every MCP-compatible client can use it.

It's USB-C for AI tooling.

The shape of it

MCP defines three concepts:

  • Resources — read-only data the model can fetch (files, query results, documents).
  • Tools — actions the model can invoke (write file, run query, send message).
  • Prompts — templated prompts the server exposes to the client.

A server exposes some combination of these over JSON-RPC. The client (Claude Desktop, Cursor, your custom app) connects to one or more servers and surfaces their capabilities to the model.

Why it matters

  • No more bespoke integrations. One server works with every client.
  • Local-first by default. MCP servers usually run on your machine, with your credentials, against your data — nothing leaves your laptop unless you say so.
  • Composable. Add a new server (filesystem, database, Linear, Slack) the same way every time.

Concrete example

A filesystem MCP server exposes:

  • Resource: file://path/to/file — model can read any file.
  • Tool: write_file(path, content) — model can write to disk.
  • Tool: list_directory(path) — model can explore.

You add this server to Claude Desktop's config. Now Claude can read and edit local files. Switch to Cursor — same server, same tools, zero rewriting.

What MCP is not

  • Not a model. Not a runtime. Not an agent framework.
  • Not magic — the model still has to be smart enough to use the tools well.
  • Not a security boundary on its own — servers can do whatever you let them do.

Should you build an MCP server?

Yes, if:

  • You have a system (internal API, database, SaaS tool) that you want any AI client to be able to use.
  • You're tired of writing the same integration five times.

The protocol is small enough to read in a single sitting. Start with the official SDKs in TypeScript or Python.

Getting started

  1. Install the SDK: npm install @modelcontextprotocol/sdk (or pip install mcp).
  2. Define your resources and tools with JSON schemas.
  3. Run the server with stdio transport for local use.
  4. Add the server to Claude Desktop's config and restart.

That's the whole loop. A minimal weather-data server is ~50 lines.

Common misconceptions

The wrong-but-common takes worth correcting.

Myth

MCP is an Anthropic product.

Reality

MCP is an open spec authored by Anthropic, but it's adopted across the industry. OpenAI, IDE vendors, and open-source clients all support it.

Myth

MCP servers are always local.

Reality

Local is the default and the safest, but MCP servers can run anywhere — including hosted services. The transport (stdio, HTTP, SSE) is configurable.

Myth

MCP replaces function calling.

Reality

MCP layers on top of function calling. The client still uses the model's tool-use API; MCP just standardizes where those tools come from.

Real-world use cases

  • Local file access for any AI client

    The filesystem MCP server gives Claude Desktop, Cursor, or your custom client the same file-read/write capabilities.

  • Database connectivity

    Connect Postgres, BigQuery, or Snowflake to your AI client — query results stream back as resources.

  • SaaS integrations

    Linear, Slack, Sentry, GitHub — each has community MCP servers that work with any compatible client.

  • Internal tooling

    Wrap your company's internal APIs as an MCP server. Every team's AI tool gets the same capabilities.

Frequently asked questions

Not strictly. MCP is most useful when the same tools need to work across multiple clients (Claude Desktop + Cursor + your custom agent). If your app only has one client, plain function calling is fine.

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