A common plug for AI systems
AI models are only as useful as the information and tools they can reach. A chatbot that cannot read your files, query a database, or trigger a workflow is limited to whatever it absorbed during training. The Model Context Protocol, or MCP, is designed to remove that limitation in a standardized way.
MCP is an open-source standard for connecting AI applications to external systems 1. Through it, assistants such as Claude or ChatGPT can reach data sources like local files and databases, tools like search engines and calculators, and workflows such as specialized prompts. That access lets them retrieve key information and carry out tasks 1. The project's own analogy is a USB-C port for AI. USB-C gives electronic devices one standardized way to connect, and MCP aims to do the same for links between AI applications and the systems around them 1.
The analogy explains the appeal. Without a shared protocol, every pairing of an AI app with an outside service needs its own custom integration. With one, a tool exposed once through an MCP server can, in principle, be used by any compatible client.
From one company's project to neutral ground
MCP's governance changed significantly in December 2025. Anthropic donated the protocol to the Agentic AI Foundation (AAIF), a fund operating under the Linux Foundation 2. This was a meaningful step. Standards controlled by a single vendor often face hesitation from competitors who do not want to depend on a rival's roadmap. Placing MCP under a neutral foundation removes much of that hesitation and signals that the protocol is meant to be shared infrastructure rather than a competitive moat.
The timing suggests the move followed adoption rather than preceding it. Rivals had already embraced MCP before the handover, so the donation looks less like an effort to build support and more like formal recognition of support that already existed.
Rivals and platforms sign on
The clearest sign of MCP's momentum is who adopted it. In March 2025, OpenAI officially embraced the standard after integrating it across its products, including the ChatGPT desktop app 2. In September 2025, OpenAI added MCP support to ChatGPT apps, opening the door to third-party access inside ChatGPT 2. When a direct competitor adopts a protocol that originated elsewhere, it usually means the standard has crossed from a proposal into an expectation.
Support extends into enterprise and cloud tooling. MCP can be integrated with Microsoft's Semantic Kernel and with Azure OpenAI, and MCP servers can be deployed on Cloudflare 2. That spread across developer frameworks and hosting platforms matters as much as chatbot integrations, because it lowers the cost for companies to build and run MCP-based services.
Measuring the scale
The reported numbers from 2026 show a protocol moving into production use. In April 2026, the AAIF hosted the MCP Dev Summit North America in New York City, which drew roughly 1,200 attendees 2. The same month, Salesforce's Headless 360 platform began routing customer and agent interactions through MCP. By late May, Salesforce said it had processed 4.5 million MCP calls since launch 2.
Broader figures are larger still. By mid-2026, more than 10,000 MCP servers had reportedly been deployed in production, and the protocol's software development kits were being downloaded more than 97 million times per month 2. These numbers are described as reported figures, and download counts in particular can be inflated by automated builds. Even so, the direction is clear.
Why it matters
The two perspectives on MCP fit together. The project's own description stresses the technical idea: a single, standardized connector between AI and the outside world 1. The adoption record shows that the idea has been accepted across companies that otherwise compete hard with one another 2. There is no real conflict between the two accounts. One explains what MCP is, and the other explains why it has become hard to ignore.
My reading is that MCP is on track to become foundational plumbing for agentic AI, comparable to how HTTP underpins the web. The combination of neutral governance, adoption by competing AI vendors, support from major cloud and developer platforms, and enterprise deployments at Salesforce's scale is the pattern that typically comes before a de facto standard settles in.
The open questions are now practical ones. As AI agents gain the ability to act on databases and business systems through thousands of servers, security, permissioning, and quality control across that ecosystem will matter more than whether the protocol itself survives. The connector has largely been agreed on. The harder work is deciding what AI should be allowed to do through it.
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