Anthropic unveils MCP, a standard connecting AI and tools

Anthropic introduced Model Context Protocol (MCP) on Monday, an open protocol for connecting artificial intelligence assistants to external data sources and tools. The initiative addresses a practical problem: AI applications typically need custom integrations to access files, databases, code repositories or corporate services.

MCP proposes a common interface for those connections. Instead of developing a separate integration for every model and service, developers could create a protocol-compatible connector and reuse it across different applications.

The problem: AI doesn’t work alone

A language model can draft text, summarize information or answer questions, but it does not inherently know the contents of a Google Drive folder, the status of a GitHub project or the latest data in a database. To use that information, it needs both permission and a technical way to access it.

Until now, those connections have generally been built for specific use cases. A company that wants to give an assistant access to Slack, PostgreSQL and its internal documentation must connect each service to the AI application it has chosen. If it switches providers or creates another assistant, some of that work may have to be repeated.

Anthropic compares this situation with the early days of peripheral devices: without a shared standard, every connection requires a custom solution. MCP is intended to serve as that common layer between an AI application and the systems where information resides or actions are carried out.

How the protocol works

MCP’s architecture separates three elements. The host is the application a user interacts with, such as a desktop assistant. Inside it, an MCP client establishes a connection to an MCP server, which exposes specific data or tools.

For example, a server could allow an assistant to query a GitHub repository, read local files, run a PostgreSQL query or automate tasks in a browser. The protocol defines how those capabilities are described and requested, without requiring every application to invent its own format.

That does not mean the model receives unlimited access to connected systems. Permissions remain critical: a well-designed integration must define what information the assistant can read and what actions it can take. The standard makes the technical connection easier, but it does not replace security policies or human oversight.

Claude Desktop adds support as Anthropic publishes examples

Anthropic has added MCP support to Claude Desktop and published specifications, TypeScript and Python software development kits, as well as several reference servers. They include connectors for Google Drive, Slack, GitHub, Git, PostgreSQL, local storage and web automation through Puppeteer.

The company has also opened the protocol to other developers and providers. That decision matters because a standard is useful only if it is not confined to a single product. If AI applications, data services and tool providers adopt incompatible interfaces, MCP would be just another integration; if it wins support beyond Anthropic, it could significantly reduce the cost of building assistants connected to real-world work.

A building block for more useful—and more sensitive—assistants

The announcement comes as assistants’ usefulness depends less on maintaining a conversation and more on accessing the right context. A system that knows an organization’s documents, calendars, tickets and repositories can respond more accurately and carry out tasks. In return, it concentrates risks involving privacy, excessive permissions and incorrect actions.

For companies, MCP’s appeal lies in avoiding repeated integrations and maintaining an architecture that is more interchangeable across models and applications. For developers, it could simplify the creation of connectors that work with multiple clients. And for users, the promise is that an assistant will no longer ask them to copy and paste information between tools.

The real test will be adoption beyond Anthropic and the quality of the implementations. A common protocol could make AI connected to corporate data more accessible, but its value will depend on ensuring that this convenience does not lead to opaque or difficult-to-monitor access.