Lune - AI Agent Science Grounding Service

Provide an MCP server that grounds AI agents in peer-reviewed research literature and best practices from notable scholars. Enables AI agents to perform science-backed tasks with credibility and accuracy for research, education, and professional use.

MCP (Model Context Protocol) adoption is accelerating fast in 2025, and the gap between AI agents that hallucinate citations and ones that can actually retrieve and ground responses in verified literature is a real and growing pain point for research and edtech use cases. Semantic Scholar and Elsevier's existing APIs are the closest substitutes, though neither is packaged as an agent-native grounding layer with MCP integration, leaving genuine white space. The $5k–20k/mo revenue band is plausible if sold as a B2B API to edtech platforms or enterprise research tools, but getting there requires institutional contracts that have long sales cycles, making solo bootstrapping slow. The biggest risk is that foundation model providers — OpenAI, Anthropic, Google — build native retrieval-augmented grounding against academic databases directly into their platforms, commoditizing the core value before a defensible customer base is established.