exist Google I/O 2025 Gemini is more than just a model, it is a set of intelligent agents that can be connected to real-world tasks.
The key technology behind making AI truly "agent-capable" is the Model Context Protocol(MCP in short)The
This article will give you an in-depth understanding of the technical principles of MCP, the application architecture and the logic of integration with Gemini models.
and analyze its actual role in multitasking and product applications.
What is Model Context Protocol? Core Concepts and Design Objectives
MCP is Google's context management and task concatenation protocol for the Gemini family.
Its main goal is to create a "contextual memory mechanism" that AI can understand and use consistently across different products and services.
How does MCP change the dialog logic of the model?
Traditional language models deal with problems as "one round at a time" interactions.
However, MCP allows the Gemini model to understand the multi-tasking context and even extend the user's historical behavior across tools:
- Check meeting times in Gmail → Schedule in Calendar
- Plan routes in Maps → Synchronize destinations to Keep memos
This behavioral flow is enabled by the cross-tool task memory provided by MCP.
And such contextual harmonization is also consistent with the Google AI Capabilities Technology Explained This is closely related to the "agency capacity" mentioned above.
MCP Operational Architecture: Tandem Logic with Gemini and Agent Models
The role of the MCP is not just to "deliver the message" but to help the AI model understand it:
- What is the context of the user's current assignment?
- What tools have you interacted with?
- Which tasks have not yet been completed?
- What information can be reused?
Bridging Models, Applications and Service APIs
MCP is essentially a protocol layer that connects Gemini models to product APIs (e.g. Docs, Gmail, Tasks, etc.), allowing the model to dynamically choose to run a service or produce a response based on a command, and remember what you just said or did.
This is not the same as Google Search What is AI Mode Deep Search, Search Live, and other multilayer semantic processing approaches mentioned in this article are consistent with the logic.
MCP Application Scenarios and Advantages
MCP is not an abstract technology; it is already practically integrated in applications such as the Gemini App, Chrome Gemini in Page, Search Live, and is expanding to more mobile and XR devices.
Examples of usage scenarios
| Application Scenarios | Tasks handled by MCP |
|---|---|
| Plan your trip in the Gemini App | Integrate Maps, Calendar, Keep and track progress. |
| Asked a question while reading a document on Chrome | Understand the current reading page, record the conversation history |
| Using Flow to Create Video Scripts | Continuously memorize themes, styles, and characterizations to reinforce narrative consistency. |
This application also supports applications such as Google AI Creation Tools Overview The AI will be able to remember and continuously optimize the creative process, instead of just "producing" it.
Integration of MCP with Deep Think and Agentic Capabilities
Although MCP itself does not directly affect the depth of inference of the model, it is a prerequisite for the operation of the Deep Think model, providing the contextual information and task background needed for inference.
If the user opens the Gemini Deep Think modelThe model can then be combined with MCP information for multi-step logic computation and task planning.
The MCP allows the Agent to truly "remember."
In the AI agent architecture defined by Project Mariner, the MCP is the most important coordination layer between the model and the application interface, and is responsible for task continuation, command assignment, and result feedback, allowing the AI to perform continuous actions rather than passive responses.
Conclusion: MCP is the key to making AI an "assistant" rather than a "tool".
With the Model Context Protocol, Google is making Gemini more than just a language model for answering questions; it can manage task flow, understand user habits, and collaborate across multiple platforms.
This is a technological leap in the evolution of AI from a tool to a true "assistant".
If you're interested in how these capabilities will be used on hardware platforms in the future, read on! Google Hardware PlatformLearn how XR devices and MCPs can create a seamless AI experience.
