The MCP (Model Context Protocol) 2025 Ecosystem Landscape: From Open Source to the Triumvirate of OpenAI, Anthropic, and Google

The article discusses the rapid adoption and evolution of the Model Context Protocol (MCP), an open standard developed by Anthropic in 2024 to connect AI models with external data sources, tools, and business systems. It highlights the key milestones, core use cases, and technical details of MCP's integration with major AI platforms like OpenAI and Google.

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Why it matters

MCP's rapid adoption by the leading AI companies demonstrates the industry's urgent need for a standardized tool integration protocol. It is poised to become the mainstream solution for connecting AI models to external systems and data.

Key Points

  • 1MCP is an open standard that enables connecting AI models to real-time data and external tools
  • 2By 2025, MCP has been fully adopted by the 'big three' AI companies - Anthropic, OpenAI, and Google
  • 3Key use cases include database querying, code repository integration, enterprise tool chain integration, and intelligent memory systems
  • 4Technical advancements like 'Context Engineering' and 'Programmatic Tool Calling' are driving MCP's evolution
  • 5The MCP ecosystem is transitioning from a tool calling protocol to an 'agentic app runtime'

Details

The Model Context Protocol (MCP) was introduced by Anthropic in November 2024 as an open standard to connect AI models with external data sources, tools, and business systems. It addresses the fundamental issue of AI models being isolated from real-time data. By 2025, MCP has been fully adopted by the 'big three' AI companies - Anthropic, OpenAI, and Google. Anthropic was the original creator, while OpenAI and Google integrated MCP into their respective platforms (Agents SDK, Gemini) in 2025. A key milestone was the joint release of the 'MCP Apps Extension' by Anthropic and OpenAI in November 2025, which introduced standardized interaction UI capabilities and upgraded MCP to an 'agentic app runtime'. The article covers core MCP use cases such as database querying, code repository integration, enterprise tool chain integration, and intelligent memory systems. It also delves into the technical advancements driving MCP's evolution, like 'Context Engineering' at OpenAI and 'Programmatic Tool Calling' at Anthropic. The article concludes by highlighting the rapid adoption of MCP and its transition from a tool calling protocol to a platform for building complex, multi-step AI agents.

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