MCP Server with Gemini AI Integration
by walnashgit
This project implements a Multi-Component Platform (MCP) server with Gemini AI integration, allowing users to perform various mathematical operations and complex tasks through natural language commands. It leverages natural language processing to execute tasks.
Last updated: N/A
What is MCP Server with Gemini AI Integration?
This is a Multi-Component Platform (MCP) server integrated with Gemini AI, enabling users to perform mathematical operations, string processing, and even interact with applications like Keynote using natural language commands.
How to use MCP Server with Gemini AI Integration?
- Clone the repository. 2. Create a virtual environment and install dependencies. 3. Set up your Gemini API key in a .env file. 4. Run
mcp_server.pyandtalk2mcp.pyseparately, or simply runtalk2mcp.pywhich will start the server if it's not already running. 5. Enter your query when prompted by the client.
Key features of MCP Server with Gemini AI Integration
Mathematical Operations (basic, advanced, special functions, list operations)
String Processing (ASCII conversion)
Keynote Integration (open, draw rectangles, add text)
AI-Powered Task Execution (natural language processing, iterative problem solving, automatic tool selection)
Use cases of MCP Server with Gemini AI Integration
Performing mathematical calculations via natural language
Automating tasks in Keynote using voice commands
Converting strings to ASCII values using natural language
Building custom tools and integrating them into the MCP framework
FAQ from MCP Server with Gemini AI Integration
What is the purpose of the .env file?
What is the purpose of the .env file?
The .env file stores your Gemini API key, which is required for the AI-powered task execution.
Do I need macOS to use this server?
Do I need macOS to use this server?
macOS is required for the Keynote integration features. The other features should work on other operating systems.
How does the server handle errors?
How does the server handle errors?
The system includes comprehensive error handling, including timeout handling for AI responses, type conversion validation, tool availability checking, and parameter validation.
Can I contribute to this project?
Can I contribute to this project?
Yes, you can contribute by forking the repository, creating a feature branch, committing your changes, pushing to the branch, and creating a Pull Request.
What license is this project under?
What license is this project under?
This project is licensed under the MIT License.