MCP is a standard that lets AI assistants connect to external tools, databases, and services. Think of it like a USB standard — it defines how things plug in, so you only have to build the connection once.
For a while, every AI integration was a custom job. If you wanted Claude to read your Notion documents, someone had to build a Notion-specific integration. If you wanted it to query your database, you needed a custom connector for that database. If you wanted it to interact with your CRM, GitHub, Slack, or any other tool, each one required separate engineering work.
This worked, technically. But it didn’t scale. Every new combination of AI model and external tool meant more custom code, more maintenance, more fragility. The integrations didn’t transfer between tools. If you built something for Claude, it didn’t automatically work with another AI model. If someone else built something for GPT-4, you couldn’t use it with Claude.
That’s the problem Model Context Protocol was built to solve.
MCP is an open protocol developed by Anthropic and now broadly adopted across the AI tooling ecosystem. It defines a standard way for AI models to communicate with external tools and data sources.
An MCP server exposes a set of capabilities: tools the AI can call, resources it can read, and prompt templates it can use. An MCP client is the AI application that connects to those servers. Once you have both, any compatible client can use any compatible server.
The analogy that makes this concrete: HTTP standardized how web browsers talk to web servers. Before HTTP, every browser and every server had to negotiate their own connection protocol. After HTTP, you build one browser and one server, and they work together automatically. MCP is doing the same thing for AI integrations. Build an MCP server once, and any AI tool that supports MCP can use it.
With an MCP server for your database, Claude can run queries directly in a conversation. You ask it to find customers who haven’t purchased in 90 days, it writes the query, runs it, and returns the result. You don’t copy-paste data into the chat window.
With an MCP server for Notion, Claude reads your docs without you having to share them manually. With one for your email, it can search through messages, draft replies, and send them. With one for GitHub, it can fetch pull requests, read issues, check commit history, and open new PRs.
Claude Code supports MCP natively. Within a Claude Code session, you can connect to MCP servers and Claude will use them as part of its workflow. Ask it to implement a feature and check the GitHub issues for context, and it’ll do both automatically.
Adoption has grown fast. Claude Code was an early native supporter. Cursor and Windsurf both support MCP. The list of available MCP servers covers databases (Postgres, SQLite, MySQL), version control (GitHub, GitLab), productivity tools (Notion, Linear, Jira), communication tools (Slack, email), and plenty of others.
The practical result is that the “connect your AI to your tools” problem is increasingly solved infrastructure rather than a custom project. If someone has already built an MCP server for the tool you want to connect, you install it and you’re done.
Building an MCP server is straightforward. Anthropic publishes SDKs in TypeScript and Python. You define the tools your server exposes, write the logic that handles calls to those tools, and run the server. If you have an internal tool, an API, or a data source that you want AI to be able to use, an MCP server is the right approach.
This is meaningful for teams building internal tooling. Instead of building a custom integration for every AI product you want to use, you build one MCP server and it works with all of them. That’s a genuinely better model.
The tools you use are going to have “AI connectors” built on this standard. When a product says it integrates with Claude or GPT-4, what they’re often building is an MCP server. Understanding what MCP is helps you evaluate which tools are investing in real AI-native integration and which are just adding a chat window to the existing UI.
The tools doing this well give the AI actual access to the data and actions that matter. The tools doing it poorly let the AI see a summary page and call it an integration. MCP is the difference between the AI having a key to the building and having a photo of the front door.
As AI assistants become more capable and agentic, the integration layer becomes more important, not less. MCP is the best current answer to how that layer should work.
If you’re designing systems that need to connect AI to your existing tools, getting the architecture right matters more than the individual pieces. Reach out and we can help think through what the right approach looks like for your situation.
