MCP Just Hit 97 Million Installs. The Agent Era Is Here.

When Anthropic first released the Model Context Protocol in late 2024, I thought it was interesting but niche. A tool-calling standard for Claude. Useful for power users, maybe. Probably wouldn't get broad adoption outside the Anthropic ecosystem.

I was completely wrong.

MCP just crossed 97 million installs across its ecosystem of packages. OpenAI added MCP support in the Agents SDK. Google integrated it into Gemini's tool use pipeline. Microsoft baked it into Copilot and VS Code. Cursor, Windsurf, Cline, Zed. Every major coding tool now speaks MCP. What started as one company's open-source experiment became the TCP/IP of the agent era.

Why MCP Won

There were other contenders. OpenAI had their function-calling spec. Google had their tool declaration format. Every provider had their own way of letting models interact with external systems. So why did MCP become the standard?

Three reasons, as far as I can tell.

First, it was open from day one. Apache 2.0 license, public spec, reference implementations in TypeScript and Python. No vendor lock-in, no licensing games, no trademark restrictions on the protocol name. When you bet on MCP, you're betting on an open standard, not a company.

Second, it solved a real problem that developers felt every day. Before MCP, connecting an AI model to external tools meant writing custom integration code for each model and each tool. If you had 5 tools and wanted to support 3 models, that's 15 integration paths. MCP collapsed that to 5 servers and 3 clients. The math speaks for itself.

Third, Anthropic shipped Claude Code. Millions of developers experienced MCP firsthand through Claude Code's tool use, and the quality of that experience created organic demand. When developers saw how cleanly MCP handled database connections, file system access, and API calls in Claude Code, they wanted the same thing in their own applications.

The Ecosystem Is Massive

The numbers as of mid-March 2026:

There are MCP servers for everything you can think of. Databases (Postgres, MySQL, MongoDB), cloud platforms (AWS, GCP, Azure), developer tools (GitHub, Jira, Linear), communication platforms (Slack, Discord), file storage, web scraping, you name it. The long tail is filling in fast.

We track the most notable agents and frameworks on our agents directory, and MCP support has become a baseline expectation. If a new agent framework launches without MCP support, developers ask why.

What This Means for Developers

If you're building anything that involves AI interacting with external systems, you should be thinking about MCP. Here's the practical breakdown.

If you maintain a SaaS product: Build an MCP server. It's the fastest path to making your product accessible to the entire AI agent ecosystem. One integration, every model.

If you're building an AI application: Use MCP clients to connect to tools instead of writing custom integrations. Your users will thank you when they can plug in their existing MCP servers.

If you're a solo developer: Start with a CLAUDE.md file in your repos and experiment with MCP servers in Claude Code or your editor of choice. The learning curve is gentle and the productivity gains are immediate.

The Agent Era Is Not Coming. It's Here.

I keep hearing people talk about the "coming agent era" as if it's some future state we're building toward. Look at the numbers. 97 million installs. 12,000+ servers. Every major provider on board. This isn't a prediction about the future. This is a description of right now.

Every day, I see agent traffic in the TensorFeed logs. Bots pulling our JSON API, reading our llms.txt, consuming our feeds. It's still a small percentage of total traffic, but it's growing every week. And these aren't dumb crawlers. They're agents doing real work on behalf of real users.

MCP is the protocol that made this possible. It gave agents a universal language for interacting with tools. And now that the language exists, the agents are showing up everywhere.

We're tracking the full landscape on our agents page and publishing regular analysis in TensorFeed Originals. The next twelve months are going to be wild.