Google announced native Model Context Protocol (MCP) support in Gemini last week, and most people missed why this matters. MCP is Anthropic's open standard that lets AI assistants connect to any app, tool, or data source through a universal interface. Now Gemini can use the same connectors that Claude uses, which means developers only need to build integrations once instead of separately for each AI platform.
What MCP Actually Is
Think of MCP as USB for AI assistants. Before USB, every peripheral needed its own custom port and driver. USB standardized the connection layer so any device could work with any computer. MCP does the same thing for AI—it standardizes how assistants connect to external tools and data sources.
Anthropic released MCP in November as an open protocol. Instead of each AI company building proprietary integrations with Slack, Google Drive, GitHub, and hundreds of other services, MCP creates one standard interface. Developers build an MCP server once, and any AI assistant that supports MCP can connect to it.
The protocol handles everything from authentication to data formatting to function calling. An MCP server exposes what it can do (like "search files" or "create calendar events"), and the AI assistant can invoke those capabilities through standardized requests.
Why Google Adopting It Matters
When Anthropic launched MCP, the obvious question was whether other AI companies would adopt it or build competing standards. Google choosing MCP over proprietary alternatives is significant validation of the protocol.
Google has more to lose than most companies by adopting external standards. They have their own cloud ecosystem, their own productivity suite, their own developer tools. They could have built a proprietary integration layer and forced developers to choose between Gemini-specific connectors or MCP.
Instead, they're embracing interoperability. Gemini can now use the growing library of MCP servers that developers have already built for Claude. That includes connectors for GitHub, Slack, Google Drive, Postgres databases, and dozens of other services.
For developers, this is huge. Build one MCP server and get compatibility with both Claude and Gemini (and eventually other AI assistants that adopt the standard). That's dramatically less work than maintaining separate integrations for each platform.
How It Actually Works
Google's implementation uses their AI Studio and Vertex AI platforms. Developers can register MCP servers, configure authentication, and expose specific capabilities to Gemini. The AI can then invoke those servers during conversations to retrieve data or take actions.
A practical example: you're chatting with Gemini about a project. It can query your GitHub repos through an MCP server to check recent commits, pull issues from Linear, fetch Slack conversations about the project, and compile a status report—all without you manually copying data between tools.
The key is context preservation. Gemini maintains conversation state while making multiple MCP calls, so it can reason about information from different sources and synthesize answers that draw on your actual project data rather than generic information.
The Microsoft Angle
Microsoft added MCP support to Windows 11 agent infrastructure last month. Now Google is adding it to Gemini. The pattern is clear: MCP is becoming the de facto standard for AI-tool integration, which puts pressure on OpenAI.
OpenAI has been notably absent from the MCP conversation. ChatGPT still uses proprietary plugins and integrations. That creates extra work for developers who want to support both ChatGPT and MCP-compatible assistants.
There's speculation that OpenAI will eventually adopt MCP or propose modifications to the standard. But right now, they're the odd one out. Anthropic created it, Google and Microsoft adopted it, and smaller AI companies are building on it. OpenAI's proprietary approach looks increasingly isolated.
The Technical Limitations
MCP is powerful but not magic. It doesn't solve authentication complexity—developers still need to handle OAuth flows and API keys. It doesn't eliminate rate limits or API costs. And it doesn't guarantee that different AI assistants will use MCP servers equally well.
An MCP server might work great with Claude's reasoning style but poorly with Gemini's. Or vice versa. The protocol standardizes the interface, not the AI's ability to use it effectively. That's still model-dependent.
There are also security considerations. Giving AI assistants access to your GitHub repos, databases, and internal tools creates new attack surfaces. If an AI hallucinates a command or misinterprets a user's intent, it could execute actions with real consequences. MCP servers need robust permission systems and audit logging.
What Developers Should Do
If you're building AI integrations, MCP is now the obvious choice. The ecosystem is growing fast—Anthropic maintains an official directory of MCP servers, and community contributions are accelerating.
Popular servers already exist for: Slack (message history and posting), Google Drive (file search and retrieval), GitHub (repo access and issue management), Postgres (database queries), Brave Search (web search), and many more. You can use these directly or fork them as starting points.
Building your own MCP server is straightforward if you're familiar with Node.js or Python. Anthropic provides SDKs and documentation. The protocol is relatively simple—you define resources (things the AI can query), tools (actions it can take), and prompts (templates for common tasks).
The Ecosystem Implications
MCP creates a new software category: AI-native integrations. Instead of building integrations for specific apps (like "Slack integration for our product"), companies will build MCP servers that work with any AI assistant.
This shifts value from the integration layer to the AI model layer. If integrations are commoditized through MCP, differentiation comes from which AI reasons better about the data and takes more useful actions. That favors companies with better models, not necessarily those with proprietary integration ecosystems.
It also enables a new breed of automation tools. Instead of using Zapier or Make to connect apps, you could use AI assistants with MCP access to orchestrate workflows dynamically based on natural language instructions. That's a different paradigm than rigid if-this-then-that rules.
Where This Goes
I expect rapid MCP adoption over the next six months. More AI assistants will support it. More developers will build servers. More enterprise tools will offer official MCP connectors.
The question is whether OpenAI joins the ecosystem or fragments it by maintaining a proprietary approach. If they adopt MCP, it becomes the universal standard. If they don't, we end up with two incompatible ecosystems—MCP (Anthropic, Google, Microsoft) and OpenAI's plugins.
My guess: OpenAI eventually supports MCP, probably with some proposed extensions they argue improve the protocol. They'll frame it as "we've been listening to the community" rather than admitting they were wrong to go proprietary initially.
My Take
This is one of those infrastructure developments that seems boring but matters enormously. Standardizing AI-tool integration means the ecosystem can grow faster because developers don't need to build everything from scratch for each platform.
Google adopting MCP is particularly meaningful because they have the least incentive to support external standards. They could have built proprietary Gemini integrations and leveraged their massive developer ecosystem to make it work. Choosing interoperability instead suggests they value ecosystem growth over lock-in.
For users, this means AI assistants will get more capable faster. For developers, it means less integration work. For the AI industry, it means we avoided the fragmentation hell that would have resulted from every company building incompatible integration systems.
Sometimes the most important innovations are the ones that make everyone else's work easier. MCP is one of those.