If you’ve been following the AI coding tool space, you’ve probably seen “MCP” mentioned everywhere — in Google Antigravity docs, Claude Code changelogs, and developer discussions. MCP, or Model Context Protocol, is quietly becoming the standard that connects AI agents to the real world.
Here’s what MCP is, why it matters, and how it’s shaping the future of agentic AI development.
What Is MCP (Model Context Protocol)?
MCP is an open protocol that standardizes how AI models connect to external tools, data sources, and services. Think of it as a universal adapter between AI agents and the tools they need to use.
Without MCP, every AI tool needs custom integrations for every service. Want your AI agent to read from a database? Custom code. Search the web? Different custom code. Access a file system? More custom code.
With MCP, there’s one standard protocol. Any MCP-compatible AI tool can connect to any MCP server, instantly gaining access to whatever tools that server provides.
Why MCP Matters for Developers
MCP solves the integration problem that has been holding back agentic AI:
- Write once, use everywhere — build an MCP server for your API, and every MCP-compatible AI tool can use it
- Tool ecosystem — a growing library of pre-built MCP servers for databases, APIs, file systems, and cloud services
- Standardization — instead of learning different integration patterns for each AI tool, learn MCP once
- Security — MCP includes permission models so you control what AI agents can access
MCP in Practice
In Google Antigravity
Antigravity uses MCP to connect to external tools and services. When you tell Antigravity to “deploy this to Google Cloud,” it’s using MCP servers to interact with GCP APIs, manage infrastructure, and handle deployment pipelines.
In Claude Code
Claude Code supports MCP servers that extend its capabilities. You can add MCP servers for your specific tools — databases, internal APIs, monitoring systems — and Claude Code can use them autonomously during coding sessions.
Building Your Own MCP Server
Creating an MCP server is straightforward. If you have an API or service you want AI agents to access, you wrap it in an MCP server that exposes:
- Tools — functions the AI can call (e.g., “query_database”, “send_email”, “create_ticket”)
- Resources — data the AI can read (e.g., documentation, configuration files, database schemas)
- Prompts — pre-built prompt templates for common tasks
The MCP Ecosystem
The MCP ecosystem is growing rapidly. Popular MCP servers include:
- Filesystem — read and write files with permission controls
- PostgreSQL / MySQL — query databases directly
- GitHub — manage repos, PRs, issues
- Slack — send messages, read channels
- Google Cloud — manage GCP resources
- AWS — interact with AWS services
- Web Search — search and fetch web content
MCP and the Future of AI Agents
MCP is to AI agents what HTTP was to the web — a standard protocol that enables an ecosystem. As more tools adopt MCP, AI agents become more capable without requiring custom integrations for each new service.
For developers building AI applications, understanding MCP is becoming essential. Whether you’re building agents at hackathons on Reskilll or developing production AI systems, MCP is the protocol that connects your agents to the world.
The Agentic India hackathon series specifically encouraged participants to use MCP for tool integration — and the teams that did built more capable, more flexible agents than those using custom integrations.
MCP is going to be huge. Once you understand it, building tool integrations for AI agents becomes trivial. Great explainer article.
We built an MCP server for our internal API at work. Now both Antigravity and Claude Code can interact with our systems. Game changer for developer productivity.