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Ai Agent

An example project that includes various AI agents and RAG technologies, covering Gemini applications, MCP services, Flutter applications, and Vertex AI integration, providing AI development examples from basic to advanced.
Artificial intelligence chatbotsDeveloper tools#AI agent#MCP service#RAG technology#Gemini application Local.TypeScript
2 points
16

What is the MCP server?

The MCP server is a middleware framework that allows developers to build intelligent agents that can interact with AI models (such as Google Gemini). It uses Server-Sent Events (SSE) technology to achieve real-time communication and has built-in multiple practical function modules.

How to use the MCP server?

Interact with the server through the MCP client or direct API calls, send requests and receive responses from the AI model. Function calling and streaming responses are supported.

Use cases

Suitable for application development that requires AI agent functions, such as intelligent assistants, knowledge Q&A systems, data analysis, and automated task processing.

Main features

AI model integrationSeamlessly integrate large language models such as Google Gemini, supporting function calling and streaming responses
Real-time communicationRealize real-time data transmission based on Server-Sent Events (SSE)
Function modulesBuilt-in practical functions such as OpenWeatherMap weather query and BMI calculator
RAG supportSupport Retrieval Augmented Generation (RAG) function, and can integrate vector databases such as FAISS and ChromaDB

Advantages and limitations

Advantages
Simplify the development process of AI agents
Support multiple AI models and function modules
Provide real-time interaction capabilities
Flexible architecture design, easy to expand
Limitations
Requires a certain technical background to fully utilize all functions
Some advanced functions may require additional configuration
Dependent on external API services (such as OpenWeatherMap)

How to use

Start the MCP server
Run the server program and ensure that the necessary environment variables and API keys are configured
Connect to the MCP client
Use the MCP client or directly connect to the server through the API
Send a request
Send a request containing instructions to the server, such as weather query or BMI calculation
Receive a response
Get the output results of the AI model through the SSE stream or direct response

Usage examples

Weather queryGet real-time weather information by integrating the OpenWeatherMap API
Health consultationCalculate the BMI index and provide health advice
Knowledge Q&AKnowledge retrieval and answering based on RAG

Frequently Asked Questions

What running environment does the MCP server require?
How to add new function modules?
Does it support other AI models?
How to handle API call limits?

Related resources

Build an AI agent with Gemini function calling
Details how to develop an AI agent using Gemini's function calling
Develop an MCP client using Google ADK
Deeply explores how to develop an MCP client using Google Agent Development Kit
Gemini 2.5 Pro and the MCP protocol
A detailed guide on integrating Gemini 2.5 Pro with the MCP protocol
GitHub repository
Source code and examples of the MCP server
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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