Power up your LLMs: Gemini CLI and Model Context Protocol (MCP)

Model Context Protocol (MCP) Explained

Key Concepts:

• Model Context Protocol (MCP): A communication layer enabling Large Language Models (LLMs) to interact with external software programs, extending their capabilities.
• LLM (Large Language Model): An AI model trained on vast amounts of text data, capable of generating human-like text, translating languages, and answering questions.
• MCP Server: A software application that implements the MCP, providing specific tools or functionalities to the LLM.
• Tools: Specific functions or capabilities exposed by an MCP server, such as addition, subtraction, search, or task management.
• Input Schema: Defines the expected data format and types required by a tool.
• OAuth Authentication: A standard protocol for securely authorizing access to resources, ensuring that actions are performed on behalf of the correct user.
• Gemini CLI: A command-line interface for interacting with the Gemini LLM.

1. Introduction to MCP

• MCP (Model Context Protocol) is a communication layer that allows LLMs to interact with other software programs.
• It enhances LLMs by enabling them to perform tasks they are not inherently good at, such as complex math or accessing real-time data.
• MCP provides a standardized way for LLMs to communicate with external programs.

2. MCP Server and Tools

• An MCP server is created using code (e.g., JavaScript, Python) and implements the MCP.
• The server registers "tools" that the LLM can use.
• Each tool has:
  • Title: A name for the tool.
  • Description: Explains the tool's purpose to the LLM.
  • Input Schema: Defines the expected input data format.
• Example: An addition tool that takes two numbers as input and returns their sum.
• The LLM uses the tool's description to determine when to use it based on the user's request.

3. Implementing MCP

• The video demonstrates creating an MCP server with tools for addition and subtraction.
• The server is written in JavaScript, but Python examples are also shown.
• The key is adhering to the Model Context Protocol, regardless of the programming language.
• The Gemini CLI is configured to interact with the MCP server by updating its settings.json file.
• The /mcp command in Gemini CLI lists available tools.

4. Using MCP for Information Retrieval and Actions

• MCP is not limited to math; it can be used for various tasks:
  • Retrieving search results.
  • Fetching weather information.
  • Accessing data not in the LLM's training set.
• MCP can also be used to take actions:
  • Adding data to a database.
  • Pushing code to GitHub.
  • Updating to-do lists.

5. OAuth Authentication for Secure Actions

• When taking actions, MCP uses OAuth authentication to verify the user's identity.
• Example: Integrating with Linear (a project management tool).
• The user authenticates with Linear through a browser-based OAuth flow.
• Once authenticated, the Gemini CLI can interact with Linear on the user's behalf.

6. Linear Integration Example

• The video demonstrates using the Gemini CLI to:
  • List issues in Linear.
  • Assign all tasks to the user.
  • Mark a task as complete.
  • Create a new task and mark it as active.
• The user can manage Linear tasks directly from the terminal, without switching to a browser.
• The example highlights the importance of precise prompts for the LLM to select the correct tool (e.g., "list issues" vs. "list my issues").

7. MCP for Generative AI Video Creation

• MCP can be used to extend the Gemini CLI's capabilities for creating generative AI videos.
• The video demonstrates using an MCP server to create an animation for MCP and the Gemini CLI using Veo 3.
• The process involves:
  • Cloning a repository.
  • Navigating to the MCP Gen Media directory.
  • Installing the MCP server using a shell script.
  • Updating the Gemini CLI settings.
  • Prompting the Gemini CLI to create an animation.
• While text in video generation is still challenging, the results were impressive.

8. Conclusion

• MCP is a powerful tool for extending the capabilities of LLM-based AI agents.
• It allows LLMs to interact with external software programs to perform a wide range of tasks.
• The power of MCP lies in its flexibility and the potential for more MCP servers to become available.
• The video concludes by using MCP to mark the video task as complete.

Key Takeaways:

• MCP is a crucial protocol for integrating LLMs with external systems.
• It enables LLMs to overcome their limitations and perform complex tasks.
• OAuth authentication ensures secure access to resources when taking actions.
• MCP has diverse applications, including information retrieval, task management, and generative AI.
• The availability of more MCP servers will further enhance the power and versatility of LLMs.