Best MCP Tools for Developers in 2025

Why MCP is the Protocol Every Developer Should Know Right Now

If you've been building with Claude or any modern LLM, you've probably hit the same wall: how do you give the model real context from your codebase, database, or APIs without copy-pasting everything into the prompt?

That's exactly what Model Context Protocol (MCP) solves — and it's the fastest-growing pattern in AI-native development right now.

What is MCP, Actually?

MCP is an open standard by Anthropic that defines how AI models connect to external tools and data sources. Think of it as a USB-C port for AI — one standard, any tool.

Instead of writing custom integrations for every LLM, you build an MCP server once, and any MCP-compatible client (Claude Desktop, Cursor, your own app) can use it.

// A minimal MCP server in TypeScript
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
 
const server = new Server({ name: "my-mcp-server", version: "1.0.0" });
 
server.setRequestHandler("tools/list", async () => ({
  tools: [
    {
      name: "get_user",
      description: "Fetch a user by ID from the database",
      inputSchema: {
        type: "object",
        properties: { id: { type: "string" } },
        required: ["id"],
      },
    },
  ],
}));
 
const transport = new StdioServerTransport();
await server.connect(transport);

The Best MCP Servers Right Now

1. Filesystem MCP Server

The most-used MCP server. Gives Claude read/write access to your local files with configurable permissions.

Best for: Code review workflows, documentation generation, project scaffolding.

2. PostgreSQL MCP Server

Connect Claude directly to your database schema. Ask natural-language questions, get SQL back, run it.

Best for: Teams doing a lot of data analysis or report generation.

3. GitHub MCP Server

Lets the model open PRs, read issues, check CI status — all from within Claude Desktop or your agent.

Best for: Automating developer workflows, triage bots.

4. Browser Use (via Playwright MCP)

Gives the AI a real browser. Navigate, click, fill forms, extract structured data from any page.

Best for: Scraping, QA automation, competitive research agents.

Building Your Own MCP Server: When It Makes Sense

You should build a custom MCP server when:

• You have internal APIs the model needs to hit
• Your team uses a proprietary tool (CRM, ERP, internal dashboard)
• You want to give Claude access to a specific slice of your database with controlled permissions

The SDK is straightforward — TypeScript and Python both have first-class support. Most servers take under a day to build.

The Practical Takeaway

MCP is not hype. It's solving a real problem: giving AI models structured, reliable access to the context they need — without prompt stuffing.

If you're building anything AI-native in 2025, understanding MCP is table stakes. Start with the filesystem server, wire it into Claude Desktop, and within an hour you'll understand why this pattern is winning.

Next steps:

• Read the MCP spec — it's short and well-written
• Clone the official servers repo for reference implementations
• Check out how to build an MCP app from scratch for a step-by-step tutorial