Builder WrenchWhat is MCP (Model Context Protocol)?
Published: May 16, 2026
MCP (Model Context Protocol) is an open standard from Anthropic that gives AI models a consistent way to connect to external data sources and tools. Instead of building a custom integration for each AI application, you build one MCP server and any compatible AI can use it.
The problem it solves
Before MCP, connecting an AI assistant to your database, Slack, or GitHub required a separate custom connector for each combination. Three data sources times three AI apps means nine integrations to build and maintain.
MCP replaces that with one protocol. Build a server once, and any MCP-compatible AI application can connect to it.
How it works
MCP uses a client-server model with three roles:
- MCP Hosts — AI applications that want to access data (Claude Desktop, Cursor, VS Code with Cline)
- MCP Clients — the protocol layer that manages connections between the host and servers
- MCP Servers — small services you write that expose your data or tools through the protocol
Each server can expose three types of capabilities:
- Resources — read-only data (files, database records, API responses)
- Tools — functions the AI can call (search, create, update, delete)
- Prompts — reusable prompt templates for common workflows
Real examples
- A GitHub MCP server that lets AI browse repos and create issues
- A Slack MCP server that lets AI read channels and send messages
- A database MCP server that lets AI query and analyze your data
- A filesystem MCP server that lets AI read and write local files
Why this matters for your website
If you run a SaaS product or API, publishing an MCP server makes your product directly accessible to AI assistants. Claude Desktop, Cursor, VS Code with Cline, and others already support MCP, and the list grows weekly. Users of those tools can point them at your service without any custom setup on their end.
MCP is about the backend connection: AI calling your API. It is complementary to the front-facing side, which is about making sure AI crawlers can read and accurately describe your site to users.
Getting started
To build an MCP server for your service:
- Install the MCP SDK for your language (TypeScript, Python, or Java)
- Define your tools and resources
- Handle authentication
- Deploy and publish
The protocol is open source at github.com/modelcontextprotocol.
Make sure AI can find your site too
MCP handles what happens after an AI decides to use your service. The step before that is making sure AI crawlers can read your site and recommend it in the first place. Check your site's AI readiness score to see what is missing.
More Guides
- How to Make Your Website AI-ReadyEverything you need to make your site AI-friendly. Comprehensive checklist with examples.
- What is llms.txt?The new standard for telling AI crawlers about your site. Like robots.txt, but for LLMs.
- What is A2A (Agent-to-Agent Protocol)?Google's Agent-to-Agent protocol. How AI agents discover and talk to each other.
- Structured Data for AI AssistantsHow JSON-LD helps AI assistants understand and accurately describe your site. Schema types, examples, and common mistakes.
- How AI Crawlers Use Your WebsiteGPTBot, ClaudeBot, PerplexityBot, and GoogleOther explained. What they fetch, how often they visit, and what makes them cite you instead of a competitor.
- llms.txt vs robots.txt: What Each Does for AIBoth files live at your site root, but they do different jobs. One controls access, the other describes content. Why you need both.