doQment is an innovative tool designed to transform any website into an ephemeral Model Context Processing (MCP) server, facilitating seamless information retrieval for AI coding agents. By automating the conversion of web content into structured, agent-ready knowledge, doQment eliminates the need for manual data extraction and integration, streamlining the development process for AI applications. Key Features and Functionality: - Agent-Ready Knowledge: Provides clean, structured corpora from real websites, optimized for direct use by AI agents. - Instant MCP Endpoints: Generates scoped MCP connections upon crawling, ready for immediate integration into coding environments. - Simple REST Integration: Utilizes straightforward RESTful APIs to initiate crawls and retrieve data, ensuring ease of use without the need for additional SDKs or complex code. - Ephemeral by Default: Creates temporary, auto-expiring servers with scoped keys, enhancing security and resource efficiency. - Comprehensive Crawls: Captures all elements of a website, including pages, images, buttons, forms, and tables, ensuring a rich dataset for AI agents. - Predictable Billing: Operates on a pay-per-use model with prepaid credits, offering cost transparency and control. Primary Value and Problem Solved: doQment addresses the challenge of integrating dynamic web content into AI coding agents by automating the process of converting websites into MCP servers. This automation reduces the time and technical complexity associated with manual data extraction and server setup, allowing developers and AI engineers to focus on building and refining their applications. By providing ephemeral, domain-specific MCP servers, doQment ensures that AI agents have access to up-to-date, structured information, enhancing their performance and reliability.