What is Mcp Gemini

mcp_gemini is an example of an MCP server and client that utilizes the Gemini framework to create an AI-powered research assistant capable of searching and extracting information from arXiv papers.

Use cases

Use cases for mcp_gemini include assisting researchers in finding relevant literature, providing summaries of academic papers, and facilitating interactive Q&A sessions regarding scientific topics.

How to use

To use mcp_gemini, clone the repository, install the required dependencies, set up your environment variables with your Google Gemini API key, and then run the MCP server followed by the MCP client to start interacting with the AI assistant.

Key features

Key features of mcp_gemini include the ability to search arXiv papers, extract relevant information, and engage in conversational AI through the Gemini 2.0 Flash, which supports tool/function calling.

Where to use

mcp_gemini can be used in academic research, educational environments, and any field where quick access to scholarly articles and information extraction is beneficial.

mcp_gemini

Example of MCP server and client using Gemini

Background

mcp_gemini demonstrates how to build an AI-powered research assistant using the MCP (Modular Command Protocol) framework and Google Gemini LLM. The project includes:

• An MCP server (research_server.py) that provides tools for searching and extracting information from arXiv papers.
• An MCP client (mcp_client.py) that connects to the server and uses Gemini 2.0 Flash for conversational AI, including tool/function calling.

Prerequisites

• Python 3.11 or newer
• uv (for running the server)
• A Google Gemini API key (set as GEMINI_API_KEY in your environment)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/mcp_gemini.git
   cd mcp_gemini
   

2. Install dependencies:

   uv pip install -r pyproject.toml
   

3. Set up environment variables:

   • Create a .env file or export your Gemini API key:

     export GEMINI_API_KEY=your-gemini-api-key
     

How to Run

1. Start the MCP server (optional, the client can also auto-start it):

   uv run research_server.py
   

2. Run the MCP client:

   uv run mcp_client.py
   

   The client will connect to the server, list available tools, and start an interactive chat loop. Type your queries (e.g., “Find papers about quantum computing”) and the Gemini LLM will use the server’s tools as needed.

3. Exit:
   Type quit in the chat to exit.

Notes

MCP support in Gemini SDK 1.16.1 is experimental, and the API is subject to change in future versions.