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MCP is an open standard protocol released by Anthropic. It allows AI models to connect directly with external data sources so that the models can read data from and write data to the connected applications. You can find more information in the official MCP docs. More specifically, MCP includes:
  • An MCP client: a component that’s integrated within LLMs and facilitates interactions with external data sources
  • An MCP server: a lightweight program that exposes data and functionality from external systems.
  • Tools: allow LLMs to access specific data and execute functionality exposed by the MCP server in response to user prompts
mcp-basics-graphic Related: How MCP compares to APIs

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Benefits of using MCP

The Model Context Protocol offers several benefits that’ll help support widespread adoption:
  • Simplifies the build process: By providing a single, standard protocol, LLM providers and SaaS applications have a clearer and easier path to integrating with one another
  • Supports workflow definitions: It provides a structured way for LLMs to retain, update, and get context, which allows the LLMs to manage and progress workflows autonomously
  • Enhances LLM efficiency: By standardizing context management, MCP minimizes unnecessary processing for LLMs
  • Strengthens security and compliance: It offers standardized governance over how context is stored, shared, and updated across different environments
Related: Tips for using MCP