Lune

Product Introduction

1. Definition: Lune is an AI-powered research platform and Model Context Protocol (MCP) server specifically designed for scientific and technical research workflows. It functions as a specialized knowledge grounding layer, connecting AI agents and Large Language Models (LLMs) to a curated, high-quality corpus of academic literature.
2. Core Value Proposition: Lune exists to eliminate "AI slop"—hallucinations and unreliable information—from AI-assisted research. It provides trustworthy AI research, evidence-backed decision-making, and high-quality knowledge grounding by restricting sources to top-tier, peer-reviewed academic conference papers, thereby 10x'ing the efficiency and trustworthiness of literature reviews, experimental design, and academic writing.

Main Features

1. Curated A/A Conference Paper Database:* Lune's foundational feature is its rigorously filtered knowledge base. It exclusively includes full papers from top-tier (A and A* ranked) computer science and related academic conferences, explicitly excluding noise from the open web, workshops, findings, tutorials, short papers, abstracts, and demos. This ensures all generated insights are grounded in authoritative source material.
2. Full-Text Semantic Search & MCP Integration: The platform enables plain language semantic search across the entire text of all papers in its corpus. Technically, it exposes this functionality via a standards-based Model Context Protocol (MCP) server, allowing AI applications like Claude Desktop, Cursor, or Windsurf to directly query Lune's database as a tool, integrating grounded research directly into the developer's or researcher's existing workflow.
3. Citation Tracing and Research Guidance: Beyond search, Lune provides citation graph analysis, visually showing what foundational work a paper builds upon and what subsequent research it influenced. It also surfaces battle-tested research best practices contextually, offering methodological guidance relevant to the user's current query or task.

Problems Solved

1. Pain Point: Researchers and technical teams waste significant time sifting through low-quality or irrelevant sources and combating AI hallucination when using general-purpose LLMs for literature review, ideation, and writing. This leads to inefficient workflows and compromised intellectual rigor.
2. Target Audience: Primary personas include Academic Researchers (PhD students, postdocs, faculty), R&D Scientists and Engineers in tech companies, Technical Writers in deep-tech fields, and AI Engineers building agents that require factual, citable knowledge grounding.
3. Use Cases: Essential for conducting a systematic literature review with traceable sources, designing experiments based on proven methodologies, writing technical papers or grant proposals with accurate citations, and peer-review preparation by quickly validating claims and related work.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros: Unmatched source quality filter (A/A* conferences only) directly tackles the core issue of LLM unreliability. The MCP integration offers seamless workflow adoption without switching apps. Strong privacy policy with no data selling. Transparent, usage-based pricing with a generous free tier.
   • Cons: The focused corpus is currently strongest in computer science; coverage for niche sub-fields or other disciplines (e.g., life sciences, social sciences) may be limited. The daily request limits on plans, while clear, could be a constraint for very high-volume, automated research agents.

2. Key Alternatives & Differentiation:

   • General Web Search (Google Scholar, Semantic Scholar): These return mixed-quality results and lack direct, structured integration with AI agents. Lune differentiates by providing pre-filtered, high-quality results and a native AI-agent interface (MCP).
   • General-Purpose LLMs (ChatGPT, Claude.ai): These models are prone to hallucination and lack consistent citation. Lune differentiates by acting as a grounding layer for these agents, providing them with verified sources.
   • Other Research Tools (Elicit, Scite): These often focus on broader biomedical or general science literature and may use different ranking metrics. Lune differentiates with its strict A/A* conference filter for CS, its MCP-first architecture for AI workflows, and a clear freemium pricing model oriented around daily AI agent requests.

Frequently Asked Questions (FAQ)

1. What is Lune Research and how does it work with AI? Lune Research is a knowledge grounding platform that provides AI agents access to a trusted database of top-tier academic papers via the Model Context Protocol (MCP). It works by acting as a tool your AI (like Claude) can call to search for and retrieve factual, citable information, preventing hallucinations.
2. What conferences and papers are included in Lune's database? Lune's database is curated to include only full, peer-reviewed papers from A and A* ranked academic conferences in fields like computer science, machine learning, and robotics. It explicitly excludes workshops, tutorials, abstracts, and open-web content.
3. How do I integrate Lune with my AI workflow? You integrate Lune by adding its MCP server endpoint (https://mcp.luneresearch.com) to any MCP-compatible client, such as Claude Desktop, Cursor editor, or Windsurf. This allows your AI assistant to use Lune's search tools directly within your existing environment.
4. Is Lune Research free to use? Yes, Lune offers a free plan with 10 requests per day. For higher usage, paid Pro ($4.99/month) and Max ($9.99/month) plans offer 300 and 600 daily requests respectively, with discounts for annual billing.
5. How does Lune ensure privacy for my research queries? Lune has a strict privacy policy stating they never sell user data or use queries for advertising. Your search queries and interactions are kept private.