What Is MCP? The Model Context Protocol Explained for 2026

Hello, this is Hamamoto from TIMEWELL.

When the conversation turns to AI agents these days, the term "MCP" comes up almost every time. In our own meetings, more people are asking, "Should we be MCP-compatible too?" Yet when I ask back, "What exactly is MCP?", surprisingly few can give a clear answer. The acronym spread faster than the understanding behind it — a classic case of jargon arriving before substance.

Frankly, I find that a shame. MCP is not a flashy new feature. It is a quiet agreement that simply standardized the "wiring" between AI and external tools. But that quietness is exactly what makes it work. Within about a year and a half of its late-2024 debut, it became the standard foundation of the AI world. In this article, I'll break down the jargon one piece at a time, explain why MCP spread so far, how you can apply it to your own operations, and the pitfalls to know before adopting it — all with the latest information as of June 2026.

What MCP Actually Is, and Why It's Called "the USB Port for AI"

MCP stands for Model Context Protocol, a standard for connecting AI models to external tools and data sources. Anthropic published it as an open-source specification on November 25, 2024[^1]. I'm being precise here because the original version of this article had the date confused. It was proposed in November 2024 — not later.

Why was this needed? Until recently, AIs like ChatGPT and Claude had a serious weakness: they could only work with their trained knowledge and the text typed in front of them. If you wanted to show the AI your latest internal sales data, let it read a Slack conversation, or have it add a calendar event, you had to write custom integration code for every single combination of AI and tool.

This was the industry's "M×N problem." With M AI applications and N tools to connect, you could end up building M times N integrations. Three AIs and ten tools means thirty. Every new tool added created work across all the AIs. That simply does not scale on the ground.

MCP turns that multiplication into addition. Both the AI side and the tool side only need to support one common standard, MCP. Then it becomes M plus N — thirteen implementations instead of thirty. This is why it's often called "the USB port for AI." Before USB existed, printers, mice, and cameras each needed a different connector. Once USB created one common port, anything could plug into the same slot. MCP brought the same idea to the world of AI. When I called it quiet, this "just standardize the format" nature is exactly what I meant.

Then vs. Now: How MCP Changed Over the Past Year and a Half

I first wrote this article in early 2026. A lot has changed since then. Because freshness drives the value of a piece like this, let me update it carefully.

Right after launch, MCP was a "promising standard" centered on Anthropic's own products. What changed that decisively was the arrival of the big players. In March 2025, OpenAI announced MCP support across the Agents SDK, the Responses API, and the ChatGPT desktop app[^2]. The fact that two rivals converged on the same standard was itself unusual, and the impact on the industry was significant. Then in April 2025, Google DeepMind announced support in Gemini[^3]. With that, nearly all the major AIs were on board.

The specification itself matured, too. The June 2025 revision formally positioned MCP servers as OAuth resource servers and, to prevent token misuse, mandated that clients implement Resource Indicators (RFC 8707, an additional authentication spec)[^4]. The November 25, 2025 revision was the largest update since launch, adding asynchronous handling of long-running tasks, elicitation (where the AI asks the user for more information), server-side agent loops, and more, all at once[^5]. What started as a "just connect them" standard has been assembled into a foundation that can hold up in real production use.

Governance shifted as well. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation[^6]. OpenAI and Block joined as co-founders, with AWS, Google, Microsoft, Cloudflare, GitHub, and Bloomberg as supporting members. In other words, it is no longer one company's property but a neutral, shared industry standard. Standards owned by a single vendor tend to draw suspicion, but with this many major players involved in governance, MCP looks set to remain the central axis for the foreseeable future. Personally, this is the point at which I decided it was a standard worth committing to.

With governance settled, the next question is how much MCP actually gets used. A standard ultimately proves itself on exactly this point. And the spread of MCP, frankly, exceeded my expectations.

The numbers make the case. The Python and TypeScript SDKs (developer building blocks) together reach roughly 97 million monthly downloads[^7]. The number of public MCP servers keeps climbing as well. The official MCP Registry (a system for registering and discovering servers), launched in September 2025, reached nearly 2,000 entries within a few months[^7]. Counting beyond the registry, one independent survey tallied more than 17,000 public MCP servers as of the first quarter of 2026[^8].

Here's what those numbers mean in practice. Reading code on GitHub, posting to Slack, querying a database, pulling design details from Figma — for almost any "thing you'd want the AI to do," a ready-made MCP server already exists, built by someone. Rather than building an integration from scratch, you can likely just pick a compatible server and plug it in. Once an ecosystem grows this large, individual companies can no longer steer it on their own. I'd say it has clearly settled in as a standard.

Want to connect AI to your own knowledge base and databases safely, without sending confidential information outside? Our enterprise AI, ZEROCK, running on domestic servers, is one option worth considering. It applies the thinking behind MCP while keeping fine-grained control over access to your internal knowledge. Feel free to reach out for a conversation.

How MCP Works: The Roles of Host, Client, and Server

Now for the mechanics. It sounds complicated, but there are only three players.

First, the host — the AI application the user touches directly. It could be a chat interface, or an IDE (the integrated environment engineers write code in). Inside this host runs the client. The client communicates with a specific MCP server and mediates between the AI and external systems, handling the behind-the-scenes work of connection management, authentication, and data format conversion.

Then there's the server, a program placed on the external system's side, one for each tool you want to connect — CRM, database, Slack, and so on. It receives requests from the client, executes the actual API calls, and returns results. Host, client, server. Through these three exchanging messages, the AI gains the ability to use information and functions outside itself.

What a server can offer the AI falls into three categories. First, tools — concrete actions like sending email, doing calculations, or calling external APIs. Think of it as close to OpenAI's function calling (a mechanism for letting AI invoke functions). Second, resources — read-only data such as file contents or database query results, used to enrich conversational context. Third, prompts — pre-built instruction templates that help the AI perform well for specific purposes like code review or inquiry handling.

The actual communication begins with a handshake. The client connects to the server and exchanges authentication credentials and supported versions. Next, the client asks, "What tools and resources are available?" and the server returns a list. When the AI decides a user's request needs an external tool, it passes the tool name and arguments to the client, which sends a request to the server. The server runs the actual operation and returns the result, and the AI uses that to assemble its final answer. Written out, it looks like many steps, but from the user's side it's just talking to a chat box. This whole sequence runs automatically in the background.

Where It Actually Pays Off: Use Cases by Department

Enough theory — let's talk about the field. MCP pays off in moments where AI shifts from "conversation partner" to "task executor."

In sales, connecting CRM, email, and calendar via MCP lets the AI take over much of the administrative work. Ask "What are total sales this quarter?" and it queries the internal database and returns the figure; ask "Summarize our deal history with Company A" and it organizes the past records. Even scheduling next week's appointment can run end to end — from booking through the calendar integration to sending the confirmation email. What salespeople should really be doing is facing the customer in front of them, not transcribing data or juggling schedules. Removing that burden has a real impact.

On the engineering side, AI assistants connected to code repositories and CI tools (systems that run tests automatically) shine. "Open the definition of this function" pulls the file from GitHub; "Point out the major changes in recent pull requests" analyzes the diffs and generates comments. Because you can drive the development flow through dialogue alone, there's less time spent stalled.

Accounting and finance are a strong fit too. Ask "Chart the top five expense categories last month," and it cross-references the accounting system and purchase history, then formats it straight into a report for leadership. Time that went into routine monthly work can shift toward analysis and deciding next moves. Customer support can automate FAQ lookups and ticket creation, speeding up response times. The common thread is that the parts where humans used to bounce between multiple tools and stitch things together by hand are now executed by the AI in one instruction. Once you notice this, it clicks that MCP is more than a buzzword.

The Security Pitfalls You Must Know Before Adopting

I've written a lot about convenience so far, but I have no intention of recommending MCP unconditionally. It carries risks you can't ignore — and honestly, this is the part I most want to emphasize.

An MCP server is like a "checkpoint" holding access rights to multiple external services at once. Convenient, yes, but a breach here can be wide-reaching. The attack methods are concrete and already documented. A prominent one is "tool poisoning," where malicious instructions, hard for human eyes to spot, are planted in a tool's description (the text the AI reads to understand how to use the tool)[^9]. The AI reads that description faithfully and obeys, so information can be siphoned off without anyone noticing. In 2025, vulnerabilities such as MCPoison (CVE-2025-54136) and CurXecute (CVE-2025-54135) were reported, showing this kind of attack is not merely theoretical[^10].

Prompt injection (an attack that slips unauthorized instructions to the AI) is itself a formidable problem — OWASP ranks it number one on its list of security risks for LLM applications[^11]. Because MCP connects AI to the external world, it structurally widens that attack surface. That's exactly why the basics can't be skipped: use only trusted servers, scope each tool's permissions to the minimum, apply robust OAuth 2.1 authentication, and audit operation logs. Casually plugging in a random MCP server because it's convenient is, to my mind, like handing your company's keychain to a stranger.

The realistic countermeasure I'd point to is controlling where the data lives. For confidential data, hosting the MCP server on-premises or in a private cloud, so the AI is never handed the full dataset, can substantially reduce risk. Our ZEROCK is an enterprise AI built precisely on this thinking. It runs on AWS domestic servers and lets you finely manage access rights to internal knowledge. It takes in the convenience of MCP while answering the needs of Japanese companies that want to use AI without sending information outside. Convenience and governance are not a choice between one or the other.

In Closing: How to Approach MCP

Let me pull the threads together. MCP is an agreement that standardized the wiring connecting AI to external tools. Anthropic published it in November 2024, and it is now a neutral industry standard under the Linux Foundation. The major AIs all support it, servers number over ten thousand worldwide, and we've entered a stage with no turning back. Operationally, it makes AI a real executor of administrative work — while also carrying security pitfalls. A technology where convenience and risk sit back to back is my honest assessment.

So where should your company start? You don't need to aim for a company-wide rollout right away. Pick one task with clearly visible impact, try it with a trusted server, and expand while confirming safety. That sequence is, in the end, the shortest path. Especially for Japanese companies handling confidential information, I believe the design decision of where to put your data will determine the success of your MCP efforts.

If you're at the stage of "I want to connect our knowledge to AI safely" or "I'd like to talk through where to begin," we'll work alongside you.

→ Talk to us about ZEROCK

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Footnotes

[^1]: Anthropic, "Introducing the Model Context Protocol," published November 25, 2024. https://www.anthropic.com/news/model-context-protocol [^2]: WorkOS, "Everything your team needs to know about MCP in 2026" (on OpenAI's March 2025 adoption). https://workos.com/blog/everything-your-team-needs-to-know-about-mcp-in-2026 [^3]: Wikipedia, "Model Context Protocol" (on Google DeepMind's April 2025 adoption). https://en.wikipedia.org/wiki/Model_Context_Protocol [^4]: Auth0 Blog, "Model Context Protocol (MCP) Spec Updates from June 2025." https://auth0.com/blog/mcp-specs-update-all-about-auth/ [^5]: WorkOS, "Everything your team needs to know about MCP in 2026" (on the 2025-11-25 spec revision). https://workos.com/blog/everything-your-team-needs-to-know-about-mcp-in-2026 [^6]: WorkOS, "Everything your team needs to know about MCP in 2026" (on the December 2025 Linux Foundation handover). https://workos.com/blog/everything-your-team-needs-to-know-about-mcp-in-2026 [^7]: digitalapplied, "MCP Adoption Statistics 2026" (on SDK downloads and MCP Registry entries). https://www.digitalapplied.com/blog/mcp-adoption-statistics-2026-model-context-protocol [^8]: digitalapplied, "MCP Adoption Statistics 2026" (on the Q1 2026 independent survey of 17,468 servers). https://www.digitalapplied.com/blog/mcp-adoption-statistics-2026-model-context-protocol [^9]: Security Boulevard, "MCP security: How to prevent prompt injection and tool poisoning attacks." https://securityboulevard.com/2026/01/mcp-security-how-to-prevent-prompt-injection-and-tool-poisoning-attacks/ [^10]: TrueFoundry, "MCP Tool Poisoning (CVE-2025-54136): A Structural Vulnerability in Agent Context." https://www.truefoundry.com/blog/blog-mcp-tool-poisoning-gateway-defense [^11]: DataDome, "MCP Security: How to Stop Prompt Injection Attacks" (on OWASP LLM Top 10 ranking prompt injection #1). https://datadome.co/agent-trust-management/mcp-security-prompt-injection-prevention/