AIBase
EN
Explore
Model Context Protocol
M

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.
File systemDeveloper tools#File management#AI integration#HTTP service#Cloud deployment.Python
2.5 points
9.6K
Open Site

Overview

Content Details

Alternatives

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 formats
Supports reading multiple file formats such as .txt, .csv, .json, .xml, .docx, etc.
Streaming processing of large files
Efficiently processes large files to avoid memory issues
Gemini API integration
Integrates with the Google Gemini API to provide intelligent processing capabilities for file content
Support for cloud deployment
Supports deployment on Docker and Google Cloud Run
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
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 file
Read a text file and use the Gemini API to summarize its content
Batch process CSV files
Read 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

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
6.4K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
5.1K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
5.4K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
6.5K
4 points
P
Paperbanana
Python
7.9K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
6.6K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
6.7K
5 points
A
Apify MCP Server
The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.
TypeScript
6.7K
5 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
26.0K
4.3 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
73.6K
4.3 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
36.0K
5 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
21.7K
4.5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
65.4K
4.5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
32.9K
5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
22.2K
4.5 points
C
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
97.7K
4.7 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
English简体中文繁體中文にほんご
© 2026AIBase