MCP: The Protocol That Connected AI to Everything

In November 2024, Anthropic released the Model Context Protocol, MCP, as an open standard. The concept was straightforward: instead of each AI application building its own integration with each external tool (file systems, databases, GitHub, Slack, Stripe, and so on), MCP defined a standard client-server interface through which any AI model could discover, query, and invoke any tool that implemented the protocol. Anthropic's analogy was USB-C: a universal connector that solved device compatibility once, so that every manufacturer and every device didn't need to negotiate their own interface. MCP proposed to solve tool compatibility for AI agents once.

Before MCP, every agent framework invented its own tool integration pattern. LangChain had tools. OpenAI had function calling. Anthropic had tool use. Each was slightly different. A developer integrating an AI assistant into a corporate system might write the same Salesforce connector three times for three different frameworks. When a new tool was released, every framework needed its own integration. The duplication was friction that slowed the entire ecosystem. MCP proposed to eliminate it: build the server once, run it with any compliant client.

Adoption was faster than anyone predicted. Within six months of release, GitHub, Google Drive, Slack, Atlassian, Linear, Cloudflare, Stripe, and dozens of other platforms had published official MCP servers. The open-source community produced hundreds more. IDE integrations (Cursor, VS Code with GitHub Copilot, JetBrains) added MCP support, so that a coding assistant could natively read repositories, run tests, query issue trackers, and interact with deployment pipelines through a single standard protocol. AI agents had acquired a standardised interface to most of the enterprise software stack.

The architectural significance was that the boundary between AI and enterprise software became a protocol rather than bespoke code. Any system that published an MCP server could be connected to any MCP-compatible AI model. Integration work moved from weeks of custom engineering to hours of configuration. For organisations where the bottleneck to deploying agents was connecting them to existing systems, MCP was a step change in deployment speed. The number of tools an agent could access grew from "however many your team had time to integrate" to "however many had published MCP servers," which grew weekly.

What MCP did not solve was the harder problem: knowing which tools to use, in what order, and how to interpret the results. A model connected to fifty tools still needed to correctly select the right one, handle unexpected outputs, and recover from failures. The protocol standardised the connection layer. The intelligence layer, whether the agent could actually complete multi-step tasks reliably, remained the frontier challenge that SWE-Bench, GAIA, and production failure rates were measuring. MCP made it easier to give agents tools. Whether agents used them well was a separate, harder question.