Raindrop
OfficialProvides structured workflows for building and deploying full-stack applications with integrated document storage and search capabilities. Guides you through requirements gathering, architecture generation, and infrastructure deployment validation.
Transforms Claude Code into a complete infrastructure development platform by providing structured workflows for building, deploying, and managing full-stack applications with databases, APIs, and live infrastructure through guided requirements gathering, architecture generation, and deployment validation.
What it does
- Search documents and content using natural language queries
- Upload and manage files in SmartBuckets
- Generate application architecture from requirements
- Deploy full-stack applications with databases and APIs
- Query specific documents with natural language
- Validate live infrastructure deployments
Best for
About Raindrop
Raindrop is an official MCP server published by docs that provides AI assistants with tools and capabilities via the Model Context Protocol. Raindrop: AI DevOps to convert Claude Code into an infrastructure-as-code full-stack deployment platform, automating app This server exposes 49 tools that AI clients can invoke during conversations and coding sessions.
How to install
You can install Raindrop in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
License
Raindrop is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Tools (49)
Searches for documents in SmartBuckets. Input should be a natural language query, e.g., find all documents.
Get the next page of an existing search result, using the same request_id as the original search.
Directly ask questions about a specific document. Input should be a natural language query, e.g., what is the name of the person in the document?
Search for content (i.e., RAG input) in all documents in your smartbuckets. Should be a natural language query, e.g., find all content related to <TOPIC>.
Create a summary of a specific page from search results.