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    EasySend MCP server lets AI assistants upload, download and share files instantly. No API key or authentication needed. Upload any file type up to 1GB, get a shareable link. Tools: upload_file, upload_multiple_files, get_bundle, download_file, download_bundle, delete_file, bundle_status.
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    MIT
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    Enables users to upload files and generate tracked, shareable links directly from AI agents like Claude Desktop or Cursor. It supports publishing various file formats including text, PDFs, and images, while providing tools for artifact management and analytics.
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    Enables AI assistants to interact with Seafile cloud storage for managing self-hosted files and directories. It supports operations such as reading, writing, moving, and searching across libraries using either account-based or library-specific authentication.
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    Lets MCP clients publish a single file to a permanent, shareable link via whamlink. Supports tools for managing links, including registration, publishing, listing, updating access, replacing content, and deleting links.
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    Provides unified access to multiple file systems (local, S3, WebDAV, etc.) through OpenDAL, enabling file operations and cross-backend management.
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    Enables AI models to interact with Akave's S3-compatible storage by providing tools for managing storage buckets and objects through standardized Model Context Protocol (MCP).
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    Enables AI assistants to interact with and manage Google Cloud Platform resources including Artifact Registry, BigQuery, Cloud Build, Compute Engine, Cloud Run, Cloud Storage, and monitoring services through a standardized MCP interface.
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    🪐 MCP IPFS Server This server empowers language models 🤖 and other MCP clients to manage storacha.network spaces, upload/download data, manage delegations, and perform various other tasks by seamlessly wrapping w3 commands.
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    Enables management of Zadara VPSA Storage Arrays and Object Storage endpoints, including volume operations, snapshot creation, and bucket management. It allows users to interact with storage resources and retrieve performance metrics using natural language or custom API requests.
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    MIT