Why this server?

This server allows AI models to interact with the local file system, enabling them to read and write files, which is crucial for using files as extra context for writing documents.

Why this server?

Provides a way to store document constraints or formatting rules that could then be used by the writing agent.

Why this server?

Enables integration with Google Drive, allowing for listing, reading, and searching over files. This can facilitate using documents stored in Google Drive as context.

Why this server?

Enables LLMs to search, retrieve, and manage documents through Rememberizer's knowledge management API, allowing for incorporating existing documents and constraints into the writing process.

Why this server?

Provides RAG capabilities for semantic document search using Qdrant, allowing users to add, search, list, and delete documentation with metadata support.

Why this server?

Provides tools for reading and extracting text from PDF files, supporting both local files and URLs.

Why this server?

Provides access to organizational Sharepoint documents through the Microsoft Graph API, enabling search and retrieval of Sharepoint content for AI assistants.

Why this server?

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities. Can be adapted for other kinds of files.

Why this server?

An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.

Why this server?

Allows storing data and constraints inside a json db and accessing that data.