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Cursor AI Had a BIG PROBLEM — I Just Found the FIX

#AI Tools
2025-09-02 06:079 min read

Content Introduction

In this video, the speaker discusses their experience using the Gemini 2.5 model within the Cursor environment, addressing its limitations related to the model's knowledge cutoff and its handling of documentation. They explain how Cursor allows integration with documentation links to overcome these limitations, enhancing coding practices. The speaker shares their experience with context 7 MCP and how it manages to process documentation more effectively but occasionally fails to obey specific instructions, leading to irrelevant results. They highlight the benefits of using Git MCP to host individual GitHub repositories as dedicated MCP servers with relevant documentation. By illustrating successful implementations of the new A2A protocol, the speaker showcases how it allows for seamless interaction between specialized agents. Lastly, they encourage viewers to engage with their content, emphasizing the importance of contextual understanding in AI deployment, and express appreciation for audience support.

Key Information

  • The speaker discusses their experiences using the Gemini 2.5 model and its limitations, including its knowledge cutoff date.
  • Cursor enhances the model's capabilities by allowing users to link documentation, but the model sometimes fails to process large data contexts effectively.
  • The speaker experimented with context 7 MCP, which improved document retrieval but still had inconsistencies.
  • Cursor integrates with tools hosted on GitHub to better interact with coding frameworks, streamlining the coding process.
  • The implementation of GitHub MCP tool and its connection with the A2A protocol was highlighted as efficient in building agents for different purposes.
  • The setup process for using the GitHub MCP tool is simple and user-friendly, enabling quick agent deployment.
  • The speaker illustrates through examples how well the system routes requests between agents for accurate information.
  • They emphasize the effectiveness of the A2A protocol in facilitating communication between agents without errors.

Timeline Analysis

Content Keywords

Cursor

Cursor is a tool that enhances programming efficiency by connecting documentation and allowing users to link various resources. It aims to address the limitations of models with fixed cutoff dates by integrating newer tools and libraries.

Gemini 2.5 model

The Gemini 2.5 model is a language model with a cutoff date, lacking awareness of recent tools or libraries unless they are manually explained or linked, leading to context issues during processing.

MCPU's library

When working with the MCPU's library, users can add documentation links which Cursor can read; however, loading all the documentation at once can create context confusion.

Context 7 MCP

Context 7 MCP is a solid platform that provides updated documentation and uses retrieval augmented generation to deliver relevant information for improved coding experiences.

A2A Protocol

The A2A Protocol enables interaction between agents, facilitating communication on specific topics, such as animals and plants. The system correctly routes requests based on context to provide relevant answers.

Git MCP

Git MCP is a tool that allows self-hosting servers to manage GitHub repositories seamlessly, enabling improved context delivery for AI applications without the need for exhaustive manual setups.

Agents

Agents are dynamic entities created to interact using specific protocols, capable of processing various inquiries across dedicated servers while maintaining organized responses and efficiency.

Documentation Handling

Documentation is critical for tools like Cursor and Git MCP to provide contextually accurate instructions. Efficient handling ensures that relevant information is correctly navigated and retrieved.

Integration and Setup

Setting up integration with Git MCP and agents was streamlined, requiring minimal effort from users while effectively generating necessary files and documentation for use.

Related questions&answers

What is the Gemini 2.5 model?

The Gemini 2.5 model is a language model that has a cutoff date, meaning it does not have knowledge of newer tools or libraries unless manually explained.

How does Cursor enhance the capabilities of the Gemini model?

Cursor enhances the capabilities by allowing users to link documentation, thus enabling the model to read and process external information more effectively.

What challenges did the user face when using the Gemini 2.5 model?

The user faced challenges such as the model loading the entire documentation at once, which created problems with context and confusion.

What is the Context 7 MCP?

Context 7 MCP is a platform that hosts updated documentation and uses retrieval augmented generation to serve relevant information.

What problem arises when linking documentation to Cursor?

The issue is that Cursor can load entire documentation and process everything at once, which may lead to confusion and loss of context.

How can Git MCP help in the context of using GitHub repositories?

Git MCP transforms any GitHub repository into its own dedicated MCP server with focused documentation, enabling better access to relevant information.

What was a notable observation about the interaction between agents?

The interaction demonstrated that the main agent successfully communicated with the second and third agents using the A2A protocol, routing requests correctly.

What advantages does the user mention regarding the Git MCP tool?

The Git MCP tool provides accurate instructions for using the A2A protocol, facilitates easier coding processes, and generates necessary files seamlessly.

What did the user find impressive about the retrieval process?

The user found that the retrieval process was impressive as it continuously fetched relevant information while building agents.

What is a key benefit of having agents communicate specifically?

Agents communicating specifically allows for more accurate and contextually relevant responses, as shown when the animal agent correctly answered a question about lions.

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