What Are MCP Servers? A Beginner's Guide

If you have been working with AI coding assistants, you have probably heard the term "MCP server" thrown around in developer communities. But what exactly are they, why do they matter, and how do you start using them? This guide breaks it down from first principles.

What Is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI assistants — like Claude Code — connect to external data sources and tools through a standardized interface. Think of it as a USB port for AI: instead of every tool needing its own custom integration, MCP provides one universal protocol that any tool can speak.

Before MCP, if you wanted an AI assistant to read your database, search your codebase, or interact with a third-party API, you needed bespoke integrations for each one. MCP replaces that fragmentation with a single, well-defined protocol.

How MCP Servers Work

An MCP server is a lightweight program that exposes a specific capability through the MCP protocol. Here is the flow:

1. You install an MCP server — this could be a server that connects to a database, an API, a file system, or any other data source.
2. Your AI assistant discovers it — Claude Code (or another MCP-compatible client) detects the server and learns what tools it provides.
3. The assistant uses it when relevant — when you ask a question that requires data from that source, the assistant calls the MCP server's tools automatically.

For example, if you install an MCP server for PostgreSQL, Claude Code can query your database directly when you ask "What are the top 10 users by signup date?" You do not need to manually copy-paste query results.

What Can MCP Servers Do?

MCP servers can expose three types of capabilities:

Tools

Functions the AI can call — like running a SQL query, searching an API, creating a file, or sending a message. Tools are the most common MCP capability.

Resources

Read-only data the AI can access — like documentation pages, configuration files, or reference datasets. Resources give the AI context without requiring a function call.

Prompts

Pre-built prompt templates that guide the AI's behavior for specific tasks. A code review MCP server might include a prompt template that structures the review process.

Real-World Examples

Here are some categories of MCP servers developers use today:

• Databases — PostgreSQL, MySQL, SQLite, MongoDB. Query your data without leaving the conversation.
• APIs — GitHub, Jira, Linear, Slack. Create issues, read messages, manage pull requests.
• Search — Web search, documentation search, codebase search. Give the AI access to information beyond its training data.
• File Systems — Read and write files in specific directories with proper sandboxing.
• Cloud Services — AWS, Cloudflare, Vercel. Manage infrastructure through natural language.
• Science & Research — PubMed, ChEMBL, clinical trials databases. Specialized tools for researchers.

How to Install an MCP Server

MCP servers are installed through your AI assistant's configuration. For Claude Code, you use the CLI:

# Add an MCP server (user scope — available everywhere)
claude mcp add <server-name> --scope user npx <package-name>

# Example: add the filesystem server
claude mcp add filesystem --scope user npx @anthropic/mcp-filesystem

# List installed servers
claude mcp list

# Remove a server
claude mcp remove <server-name>

Most MCP servers are distributed as npm packages and run via npx. Some use Docker containers or standalone binaries. The installation command varies by server, but the pattern is the same: tell your client where to find the server and what arguments it needs.

Finding MCP Servers

This is where it gets interesting — and where BlestLabs comes in. With thousands of MCP servers available, finding the right one for your use case can be overwhelming. The BlestLabs MCP & Skill Directory catalogs over 16,000 MCP servers with:

• Search and filtering by category, language, and functionality
• Trust scores based on repository activity, documentation quality, and community adoption
• Security badges for servers that have been verified
• One-click install commands you can copy and paste directly into your terminal

Best Practices

A few tips for working with MCP servers effectively:

Start small. Install one or two servers for your most common tasks and learn how they behave before adding more.

Use user scope. Install servers at user scope (--scope user) so they are available across all your projects, not just the current one.

Check trust scores. Not all MCP servers are created equal. Look for active repositories, recent updates, and documentation. The BlestLabs directory makes this easy with its trust scoring system.

Review permissions. MCP servers can access data on your behalf. Understand what a server can read and write before installing it. Filesystem servers, for example, should be scoped to specific directories.

What is Next for MCP?

The MCP ecosystem is growing rapidly. Anthropic continues to evolve the protocol, and the community is building new servers daily. As the standard matures, expect tighter IDE integrations, better security models, and more specialized servers for niche domains.

Whether you are a solo developer looking to speed up your workflow or a team building internal tooling, MCP servers are worth understanding. They are the connective tissue between AI assistants and the tools you already use.

Explore the full catalog at the BlestLabs MCP Directory.