Lune Research is an MCP server built to give AI agents a reliable, citable foundation in Computer Science literature. Instead of letting a model improvise from noisy web snippets, it connects agents directly to full-text, top-tier CS papers so answers are grounded in real research rather than guesswork.
What it offers
Lune Research acts as a retrieval layer for AI agents that need to reason over academic material. By serving full-text papers rather than abstracts or summaries, it gives agents the depth required for technical questions, literature reviews, and method comparisons. The result is output that reflects what the research actually says, not what a model half-remembers from training data.
Who it is for
- AI developers building agents that answer technical or scientific questions
- Researchers and students who want an assistant that can cite real papers
- Engineering teams prototyping literature-aware tools without assembling a paper corpus from scratch
Core capabilities
Lune Research focuses on grounding rather than generation. It supplies agents with full-text, top-tier Computer Science papers, which reduces hallucinated facts and filters out the noise that comes from open-web sources. Because it is delivered as an MCP server, it plugs into agent frameworks that already speak the Model Context Protocol, so teams can add a research-grade knowledge source without redesigning their stack.
Typical use cases
A coding assistant can pull from papers when explaining an algorithm. A research agent can trace claims back to source material. A study tool can answer questions about a specific method with the original text in reach. In each case, the value is the same: the agent stops guessing and starts citing.
What makes it different
Most retrieval setups lean on general web search, which mixes credible sources with marketing pages, forum posts, and stale copies. Lune Research narrows the scope to full-text, top-tier CS papers, which raises the signal-to-noise ratio for technical work. It is not a general search engine or a writing assistant; it is a focused grounding layer for agents that need to be right about Computer Science.
For teams building agents in research, education, or developer tooling, Lune Research offers a practical way to keep answers anchored to the literature.
