MCP Gemini Tutorial
This project is a tutorial for building a Model Context Protocol (MCP) server based on Google's Gemini 2.0 model. It includes a complete code implementation, demonstrating how to enable AI models to seamlessly access external tools (such as the Brave Search API) through the MCP standard, and provides an example of a flexible architecture design.
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What is Model Context Protocol (MCP)?
MCP is an open standard developed by Anthropic that enables AI models to seamlessly access external tools and resources. It creates a standardized way for AI models to interact with tools without the need for custom integration for each tool or model.How to use the MCP server?
Through simple API calls, you can connect Google's Gemini model with tools such as Brave Search to build intelligent applications. You can experience it by running the example client after installation.Use cases
Suitable for application scenarios where AI models need to access real - time network information, execute code, or use other external tools, such as intelligent assistants and research tools.Main features
Web searchImplement general Internet search functionality through the Brave Search API
Local searchUse Brave Search to find business locations and location information
Gemini integrationSeamlessly connect Google's latest Gemini 2.0 AI model
Advantages and limitations
Advantages
Interoperability: Any MCP - compatible model can use any MCP - compatible tool.
Modularity: Adding or updating tools does not require changing the model integration.
Standardization: A consistent interface reduces the complexity of integration.
Separation of concerns: The model capabilities and tool functions are clearly divided.
Limitations
Requires API keys (Brave and Google).
Currently only supports TypeScript implementation.
Tool extension requires development work.
How to use
Installation preparation
Ensure that the Bun runtime environment is installed and obtain the API keys for Brave and Google.
Clone the repository
Get the project source code locally.
Install dependencies
Enter the project directory and install the required dependency packages.
Configure the environment
Create a.env file and add your API keys.
Run the example
Try running the basic client or the Gemini integration example.
Usage cases
Network information queryLet the AI model obtain the latest network information to answer questions.
Local business searchFind specific types of nearby businesses.
Frequently Asked Questions
Do I need to pay to use this service?
Can I add my own tools?
Which programming languages are supported?
Related resources
Official MCP documentation
Anthropic's official MCP protocol documentation.
Google Gemini API documentation
The official documentation for the Google Gemini API.
Brave Search API documentation
The usage guide for the Brave Search API.
Original blog post
The original blog post for this tutorial.
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