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by serkan-ozal • Search & Retrieval
A local or remote MCP server that crawls, indexes, and returns topic-aware web and GitHub content to power Retrieval-Augmented Generation (RAG) workflows for AI assistants.
Retrieve and rank topic-tagged web documents to ground answers in recent, domain-specific content.
Search across crawled GitHub repositories, issues, and discussions to surface relevant developer or project information.
An easily integratable MCP data source (via stdio or HTTP) to enable RAG-style workflows in tools like ChatGPT, Claude, Cursor, and VS Code.
Driflyte MCP Server crawls web pages and GitHub repositories (including issues and discussions), tags documents by topic, and exposes search tools for AI assistants to retrieve topic-specific, up-to-date content. It is designed to act as a bridge between diverse content sources and AI reasoning workflows, enabling richer grounding for generated answers. The server supports STDIO and streamable-HTTP transports and is intended for easy integration with MCP-capable clients like ChatGPT, Claude, Cursor, VS Code, and others.
Returns a list of topics for which resources (web pages, etc ...) have been crawled and content is available. This allows AI assistants to discover the most relevant and up-to-date subject areas currently indexed by the crawler.
Given a list of topics and a user question, this tool retrieves the top-K most relevant documents from the crawled content. It is designed to help AI assistants surface the most contextually appropriate and up-to-date information for a specific topic and query. This enables more informed and accurate responses based on real-world, topic-tagged web content.
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