Hide is an MCP (Model Context Protocol) server that provides tools for file editing and bash shell operations. It is designed to integrate with Claude Desktop and supports functionalities like text editing, directory listing, file creation, string replacement, and persistent bash execution.
Hide is used to provide developers with a powerful environment for managing files and executing bash commands in a structured manner. Its integration with tools like Claude Desktop makes it suitable for development workflows involving AI-assisted coding or context-based operations.
Developers, especially those working with AI models or requiring robust file manipulation and bash capabilities, would benefit from using Hide. It is particularly useful for teams leveraging the Model Context Protocol (MCP) for their workflows.
Hide can be installed and run on systems where Python and its dependencies are supported. It integrates with Claude Desktop, which has configurations available for MacOS and Windows operating systems.
Hide should be used when there is a need for advanced file manipulation, persistent bash shell usage, or integration with AI-driven workflows via the Model Context Protocol (MCP). It is ideal during development, debugging, or deployment phases of software projects.
To install Hide, ensure you have the necessary dependencies and Python version as specified in `.python-version`. Configuration involves setting up `claude_desktop_config.json` on MacOS or Windows and defining the `mcpServers` section appropriately for development or published servers.
Hide includes two primary tools: a Text Editor for viewing and editing files with features like line numbers, directory listing, and edit history; and a Bash shell with support for Linux/Python packages, background processes, and automatic output truncation.
For effective debugging, use the MCP Inspector tool. Launch it via `npx @modelcontextprotocol/inspector uv --directory /path/to/hide-mcp run hide-mcp`, and access the displayed URL in your browser to interactively debug the server.
To create a standalone executable, run `uv run pyinstaller hide-mcp.spec`. This will generate the executable in the `dist/` directory, making it easy to distribute or deploy.
To publish Hide to PyPI, first sync dependencies using `uv sync`, build the package with `uv build`, and then publish using `uv publish`. Ensure you have set PyPI credentials via environment variables or command flags for authentication.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.