What is an MCP Server? A Plain-English Guide for Developers
If you've been following the AI development space lately, you've probably seen "MCP" mentioned in the context of Claude, Cursor, and other AI coding tools. The term gets used a lot, but clear explanations are harder to find. This article is a straightforward guide: what MCP is, how it works under the hood, and why it matters for developers.
The Problem MCP Solves
AI language models are powerful at generating text, reasoning about problems, and writing code. But they're isolated by default. They don't have access to your filesystem, your APIs, your databases, or your tools unless someone explicitly builds that connection.
Before the Model Context Protocol, adding tool access to an AI agent meant custom integrations — often specific to one AI provider, often brittle, always requiring maintenance as the AI's API changed.
MCP is a standardized protocol for connecting AI agents to external tools and data sources. Instead of every integration being bespoke, MCP defines a common interface that any AI client can speak and any tool provider can implement.
How MCP Works
The architecture is straightforward. There are three pieces:
MCP Host — the AI application the user interacts with (Claude Desktop, Cursor, Windsurf, a custom app)
MCP Client — built into the host, handles communication with servers
MCP Server — a program that exposes tools, resources, and prompts to the AI
The server runs locally (or remotely) and communicates with the AI client over a standard protocol. The AI discovers what tools are available, their descriptions, and their input schemas. When the user (or the AI itself) decides to use a tool, the AI sends a structured call to the MCP server, which executes the operation and returns the result.
From the AI's perspective, an MCP server looks like a set of capabilities it can invoke. From the developer's perspective, an MCP server is a program that receives structured requests and does something useful with them.
What MCP Servers Can Expose
The protocol defines three types of capabilities:
Tools — functions the AI can call to perform actions. A tool might read a file, query a database, call an external API, run a script, or create a document. Tools can have side effects.
Resources — read-only data sources the AI can access. A resource might be a file, a database record, a URL, or any structured data. Resources are designed for the AI to read context, not to trigger actions.
Prompts — reusable prompt templates that the AI can use. Less common in practice, but useful for standardizing how the AI approaches specific tasks.
Examples of MCP Servers
The ecosystem has grown quickly. Some categories:
Filesystem tools — let the AI read and write files on your local machine with explicit permissions. Anthropic ships an official @modelcontextprotocol/server-filesystem package.
Database tools — connect the AI to PostgreSQL, SQLite, or other databases. The AI can query, inspect schemas, and run migrations through structured tool calls.
Browser automation — let the AI control a headless browser, take screenshots, fill forms, and scrape pages.
GitHub integration — let the AI create issues, open pull requests, read repository contents, and manage branches.
Dev workflow tools — tools for running tests, checking types, running linters, and other development operations.
Planu — an MCP server specifically for Spec Driven Development workflows. It gives AI agents the tools to create and manage software specifications, track implementation progress, analyze codebases, and validate that implementations match specs.
Why Developers Build MCP Servers
The compelling thing about MCP is that it's not hard to build a server, and the surface area of what's useful is enormous.
If there's a tool or data source you interact with regularly, and you want your AI agent to be able to interact with it too, an MCP server is the right abstraction. The AI doesn't need a special prompt or a clever workaround — it just has a tool it can call.
Practical reasons developers build MCP servers:
- Automate repetitive tasks — instead of copy-pasting output between your terminal and your AI, the AI can run the commands directly
- Give AI access to proprietary data — internal APIs, databases, or documentation that the AI doesn't have access to by default
- Enforce workflows — an MCP server can encode a process (like the SDD workflow in Planu) and make the AI follow it consistently
- Build team tooling — share an MCP server with your team so everyone's AI agent has the same capabilities and constraints
How to Use an Existing MCP Server
If you want to add an MCP server to Claude Desktop or another compatible host, the setup is typically a configuration change. For npm-published servers, it looks like:
{
"mcpServers": {
"planu": {
"command": "npx",
"args": ["--prefer-online", "-y", "@planu/cli@latest"]
}
}
}This goes in your AI client's configuration file. The client starts the server process when it launches, and the AI immediately has access to the server's tools.
Most MCP servers are either:
- npm packages (Node.js, run with
npx) - pip packages (Python, run with
uvxorpython -m) - Local scripts (any language, pointed to with a path)
The protocol is language-agnostic — the server just needs to speak MCP over stdio or HTTP.
The Broader Ecosystem
Anthropic introduced the Model Context Protocol in late 2024 and open-sourced the specification. Since then, it's been adopted by:
- AI clients: Claude Desktop, Cursor, Windsurf, Zed, VS Code (via extensions), and others
- Tool providers: dozens of community-maintained servers for databases, cloud providers, dev tools, and more
- AI frameworks: LangChain, LlamaIndex, and others have added MCP support
The protocol has enough traction that it's becoming the default way to extend AI agents with external capabilities, similar to how REST became the default way to expose web APIs.
Getting Started with Planu
Planu is an MCP server that brings Spec Driven Development into your AI workflow. Once installed, your AI agent can create and manage software specs, track implementation progress, audit your codebase, and verify that implementations match their intended design.
Installation
Planu works with Claude Desktop, Cursor, Windsurf, Gemini CLI, and any MCP-compatible host.
Read the Getting Started guide to add Planu to your setup in five minutes.
MCP is the plumbing. What you build with it is up to you.