Model Context Protocol standard

The Model Context Protocol, or MCP, is a standard that enables AI models to access software, files, and online services through a common interface. It allows AI assistants, like Claude Code or Gemini CLI, to perform tasks such as looking up a user’s calendar or retrieving documents, rather than just explaining how to do it manually.

Common interface for AI tools

Anthropic introduced the MCP in November 2024, comparing it to USB-C, a universal connector for devices. The protocol proposes a single way for AI assistants to interact with various services, without requiring unique connections for each one. It doesn’t replace APIs but can use them or directly access files and databases.

How MCP improves efficiency

The MCP allows an AI to use a calendar’s API to search for meetings, view events, or create new ones. This simplifies development for programmers and makes it easier for users to connect services with compatible assistants. However, it doesn’t fix errors in AI reasoning, and different MCP-compatible applications may still have varying features and permissions.

Behind the scenes of MCP

When an AI uses the MCP, it can search a user’s calendar, find related files, and summarize them. This happens through an MCP client communicating with one or more MCP servers, which can be small software running on a local device rather than a distant server. The servers provide tools, resources, and reusable prompts for tasks.

MCP and autonomy

The MCP doesn’t make a chatbot fully autonomous on its own. It allows assistants to perform actions like consulting reports and creating tasks in project management software. However, the extent of autonomy is controlled by the application hosting the AI, which decides when to validate actions or block them.

MCP vs. A2A standard

MCP should not be confused with the A2A standard, which focuses on communication between AI agents. These two standards can coexist, with one agent using MCP to access a calendar and another using A2A to delegate a search task. They serve different purposes but complement each other.

User-friendly MCP usage

The MCP is designed to be accessible even to non-coders. Users can connect services through assistants without ever seeing an MCP server, simply by granting permissions. Some tools, like Claude Desktop, allow local extensions without complex configurations. However, integrating custom or internal tools may require development and maintenance.

MCP across major platforms

Each major AI platform—like Claude, ChatGPT, Gemini, and GitHub Copilot—has its own implementation of the MCP. While the protocol is common, the available tools and functions vary depending on the specific application and company policies.

Costs and complexity

Connecting an AI to multiple services through the MCP can increase costs due to the need to transmit more data, which uses more tokens. This can slow down responses and raise bills, especially with many tools or large documents involved. Efficient systems only connect necessary tools and filter data before sending it to the model.

Security and data privacy

When connecting an assistant to an MCP server, users must be cautious about which data is accessed and who controls it. The "MCP compatible" label does not guarantee security, and users should limit permissions and ensure that sensitive actions are validated by humans. The MCP itself does not replace the privacy protections of the services involved.

MCP's future and adoption

Anthropic donated the MCP to the Agentic AI Foundation, aiming to make it a widely adopted standard. The protocol is evolving beyond simple tool access, enabling assistants to display interactive forms, maps, and tables. Future developments will focus on improving task handling and agent permissions, especially for AI working independently of user input.