MCP Server Box

Created By

MCP-Mirrora year ago

Mirror of

Overview

What is MCP Server Box?

MCP Server Box is a Python project that integrates with the Box API to perform various operations such as file search, text extraction, AI-based querying, and data extraction. It aims to provide a robust solution for managing and processing Box data using advanced AI and machine learning techniques.

How to use MCP Server Box?

To use MCP Server Box, clone the repository from GitHub, set up your environment, and run the server using the provided command. You will need to configure your Box API credentials in a .env file.

Key features of MCP Server Box?

Integration with Box API for file operations
Tools for searching files, reading content, and extracting data
AI-based querying capabilities
User information retrieval and application authorization

Use cases of MCP Server Box?

Searching for specific files in Box storage.
Extracting data from documents using AI.
Reading and processing text content from Box files.
Managing folder contents and structure in Box.

FAQ from MCP Server Box?

What are the requirements to run MCP Server Box?

You need Python 3.13 or higher and Box API credentials.

Is there a graphical interface for MCP Server Box?

No, MCP Server Box is a command-line tool designed for developers.

Can I use MCP Server Box for large datasets?

Yes, it is designed to handle scalable operations on Box files and folders.

Project Info

Created At

a year ago

Updated At

a year ago

Author Name

MCP-Mirror

Star

Language

Python

License

Recommend Servers

View All

Kazakhstan Payments Mcp

11 days ago

11 days ago

Create a remote sandbox that can execute code/run commands/upload and download files. 创建远程沙盒，可以执行代码/运行命令/上传下载文件

a year ago

GitLab

@modelcontextprotocol

GitLab API, enabling project management

a year ago

EdgeOne Pages MCP

@TencentEdgeOne

An MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.

TypeScript

a year ago

Test

@modelcontextprotocol

test

7 months ago

Sentry

@modelcontextprotocol

Retrieving and analyzing issues from Sentry.io

a year ago

PostgreSQL

@modelcontextprotocol

Read-only database access with schema inspection

a year ago

Aiimagemultistyle

@codecraftm

A Model Context Protocol (MCP) server for image generation and manipulation using fal.ai's Stable Diffusion model.

a year ago

11 days ago

BlenderMCP connects Blender to Claude AI through the Model Context Protocol (MCP), allowing Claude to directly interact with and control Blender. This integration enables prompt assisted 3D modeling, scene creation, and manipulation.

a year ago

Rwanda Payments Mcp

@junter1989k-ai

11 days ago

Framelink Figma MCP Server

@GLips

MCP server to provide Figma layout information to AI coding agents like Cursor

TypeScript

a year ago

Fetch

@test

Web content fetching and conversion for efficient LLM usage

9 months ago

11 days ago

11 days ago

11 days ago

Language models generate; Concordance verifies. The verify tool checks a claim deterministically (no model in the loop) and returns HOLDS / BROKEN / INCOMPLETE with the worked reasoning and a sealed receipt (content_hash + cite_url) that re-fetches byte-identical or not at all. Also: ranked search over an ~11k-record library, seal_fetch to re-verify any receipt, redact to strip PII before text travels, and a sealed connection graph. Runs sovereign/offline too (stdlib-first Python). A public false-positive benchmark covers every domain: the engine has never sealed a falsehood. 38 tools live; remote endpoint at https://narrowhighway.com/mcp.

11 days ago

11 days ago

Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.

Python

a year ago

Zhipu Web Search

@BigModel

Zhipu Web Search MCP Server is a search engine specifically designed for large models. It integrates four search engines, allowing users to flexibly compare and switch between them. Building upon the web crawling and ranking capabilities of traditional search engines, it enhances intent recognition capabilities, returning results more suitable for large model processing (such as webpage titles, URLs, summaries, site names, site icons, etc.). This helps AI applications achieve "dynamic knowledge acquisition" and "precise scenario adaptation" capabilities.

a year ago

Memory

@modelcontextprotocol

a year ago

Guatemala Payments Mcp

@junter1989k-ai

11 days ago

Bulgaria Payments Mcp

@junter1989k-ai

11 days ago

Germany Payments Mcp

@junter1989k-ai