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Model Context Protocol
This project implements an MCP server based on FastAPI, which supports interacting with the file system through HTTP requests, including file creation, reading, copying, moving, and deletion, and integrates the Google Gemini API for file content processing and summary generation.
rating : 2.5 points
downloads : 24
What is the MCP File System API?
The MCP File System API is a server built on FastAPI that allows users to manage the file system through simple HTTP requests. It supports read and write operations for multiple file formats and can integrate with the Google Gemini API to provide intelligent processing capabilities for file content.How to use the MCP File System API?
You can perform file operations, such as reading, creating, copying, moving, and deleting files, by sending HTTP requests to the server endpoints. The server also supports integration with the Google Gemini API for intelligent processing of file content.Use cases
Suitable for application scenarios that require remote management of the file system, such as cloud storage services, document management systems, and AI applications that need to automatically process file content.Main features
Support for multiple file formatsSupports reading multiple file formats such as .txt, .csv, .json, .xml, .docx, etc.
Streaming processing of large filesEfficiently processes large files to avoid memory issues
Gemini API integrationIntegrates with the Google Gemini API to provide intelligent processing capabilities for file content
Support for cloud deploymentSupports deployment on Docker and Google Cloud Run
Advantages and limitations
Advantages
Simple and easy-to-use HTTP API interface
Support for multiple common file formats
Seamless integration with AI services
Suitable for cloud-native deployment
Limitations
Currently does not support binary file processing
File operation permissions depend on server configuration
The Gemini API requires additional configuration
How to use
Clone the repository
Clone the project code to your local machine
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Set up a virtual environment
Create and activate a Python virtual environment
Install dependencies
Install all necessary Python dependency packages
Configure environment variables
Create a .env file and set the Gemini API key
Start the server
Start the MCP server using Uvicorn
Usage examples
Read and process a text fileRead a text file and use the Gemini API to summarize its content
Batch process CSV filesRead the content of a CSV file and convert it to JSON format
Frequently Asked Questions
How to obtain a Google Gemini API key?
What is the maximum supported file size?
How to deploy to Google Cloud Run?
Related resources
FastAPI official documentation
Official documentation for the FastAPI framework
Google Gemini API documentation
Official documentation for the Google Gemini API
Project GitHub repository
Source code repository for this project
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