everymcp.ai

// model context protocol

MCP is how AI tools stop being islands.

The Model Context Protocol lets your AI assistant talk to real systems — your database, your issue tracker, your browser, your cloud. Understand it once and every AI tool you use gets more capable.

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clients
ClaudeCursorVS CodeAgents
servers
GitHubSlackPostgresBrowserFiles
01MCP in one minute

One protocol, every integration.

An MCP server exposes tools and data from some system in a standard format; any MCP-capable client can connect and use them. That’s the whole idea — three lines and you’ve got it.

  1. 01

    Servers expose capabilities

    An MCP server wraps some system — a database, an API, your file system — and offers its tools and data in one standard shape.

  2. 02

    Clients speak one protocol

    Any MCP-capable client — Claude, your IDE, an autonomous agent — connects to any server without a bespoke, one-off integration.

  3. 03

    Build once, use everywhere

    Write an integration a single time and every MCP client can use it. That's the whole trick — and it's why MCP spread so fast.

02Why it caught on

It turns an integration mess into simple arithmetic.

Before MCP, connecting N AI tools to M systems meant building N × M bespoke integrations — every app rebuilding the same GitHub or Slack connector. MCP makes it N + M: each side implements the protocol once, and everything can talk.

Stop rebuilding the same connector

One GitHub or Slack server, shared by every tool — instead of each app shipping its own half-working version.

Swap tools, keep your integrations

Move from one AI client to another and your servers come with you. The protocol is the contract, not the vendor.

Work from live context

Your assistant reads the real issue, the real error, the real schema — not whatever you managed to paste into a prompt.

03Starter directory

Real MCP servers worth connecting first.

curated · growing

Names and honest one-liners — no install commands, no version claims. Check each project’s own docs to wire it up.

Filter servers by category

GitHub

Read and write issues, pull requests, and code across your repositories.

dev tools

Context7

Pull current, version-accurate library docs straight into the model's context.

dev tools

Sentry

Surface real errors and stack traces so your AI debugs from production signal.

dev tools

Figma

Bring frames, components, and design specs into your build workflow.

dev tools

Linear

Let your assistant triage issues and plan across projects and cycles.

project management

Notion

Read and update pages and databases across your Notion workspace.

project management

Slack

Read channels and post messages where your team already works.

communication

Playwright

Drive a real browser end to end — navigate, click, and fill forms.

browser

Chrome DevTools

Inspect the DOM, read console output, and profile live pages.

browser

Cloudflare

Manage Workers, DNS, KV, and object storage on your Cloudflare account.

infra

Stripe

Look up customers, inspect payments, and work with your Stripe data.

infra

Vercel

Inspect deployments, read build logs, and manage your projects.

infra
04Getting it working, for real

From “installed” to “depends on it.”

There’s a gap between “I installed an MCP server” and “my team’s daily workflow depends on one.” Closing it is mostly choosing the right servers, wiring auth safely, and building the habits — usually days of work, not quarters. I make short walkthroughs for the first part, and help teams with the second.

MCP walkthroughs on YouTube ↗Book a call about AI integration

Consulting for teams that want MCP integrated properly — not just installed.