Mountain View, CA – April 10, 2025 – In a pivotal move for the AI development ecosystem, Google has announced plans to integrate support for Anthropic’s Model Context Protocol (MCP) across its Gemini suite of AI models and developer SDKs, signaling growing industry momentum behind the emerging standard for connecting AI agents to live data systems.
The announcement came via a post on X (formerly Twitter) by Demis Hassabis, CEO of Google DeepMind, who praised MCP as a “rapidly emerging open standard” for facilitating data-aware AI workflows in enterprise environments.
“MCP is a good protocol and it’s rapidly becoming an open standard for the AI agentic era,” Hassabis said. “We look forward to developing it further with the MCP team and other stakeholders.”
What is the Model Context Protocol (MCP)?
Developed and open-sourced by Anthropic, the AI startup known for its Claude series of large language models, MCP is a specification that allows AI models to access and interact with external data sources in real-time. This includes enterprise software, content repositories, development platforms, and workflow automation tools.
At its core, MCP enables the creation of MCP servers—data endpoints that expose structured information—and MCP clients, such as AI chatbots or agentic applications, which query and update these sources during runtime.
By supporting MCP, developers can build two-way data pipelines between AI systems and business-critical apps like Slack, Salesforce, Notion, or custom analytics stacks—empowering AI models to retrieve, reason, and act upon live information in a secure, structured manner.
Widespread Adoption Gaining Speed
Google becomes the second major AI player to embrace MCP, following OpenAI, which announced its own support for the protocol just weeks earlier. Other companies already integrating MCP into their products include:
- Block (formerly Square), for financial tooling
- Apollo (developer operations)
- Replit, Codeium, and Sourcegraph (developer-centric platforms)
This wave of adoption indicates a shift toward interoperability-first AI architectures, as enterprises increasingly demand LLMs that can integrate seamlessly into existing software ecosystems.
Google’s Strategic Move
Google’s adoption of MCP aligns with its broader push toward enterprise-grade AI agents. With the Gemini AI family at the center of its AI strategy and a strong developer ecosystem via Google Cloud and Vertex AI, integrating MCP could significantly expand the utility of Google’s models in real-world applications.
Though no timeline was specified, the move is expected to influence standards discussions across organizations like the Partnership on AI, Linux Foundation’s LF AI & Data, and AI Alliance, where standardization for data access protocols is a hot topic.
The Bigger Picture: The Agentic AI Era
The integration of protocols like MCP marks a broader transition in AI from standalone chatbots to autonomous AI agents capable of reasoning, planning, and interacting with dynamic data. These capabilities are foundational for tasks like:
- Workflow automation (e.g., updating CRMs)
- Intelligent customer support
- Dynamic report generation
- Real-time software coding assistants
As developers increasingly require agentic systems with both contextual intelligence and data-action capabilities, MCP may emerge as the HTTP of AI agents—an underlying protocol standardizing how AI interacts with the world.
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