Lune Research

{
  "id": "aix_258bfdd441",
  "name": "Lune Research",
  "operator": "(unclaimed - source: registry-official · publisher: com.luneresearch)",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-11-09",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "citation-check",
    "dedupe-records",
    "sql-database",
    "text-to-image"
  ],
  "protocols": {
    "mcp": "https://mcp.luneresearch.com/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 4830,
    "uptime_30d": 0.9956709956709957,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 38,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-11-09T02:01:23.166Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://luneresearch.com/"
    },
    {
      "label": "repository",
      "url": "https://github.com/RetrogradeLabs/lune-mcp-server"
    }
  ],
  "avatar": "https://github.com/RetrogradeLabs.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/RetrogradeLabs"
    }
  ],
  "unreachable": false,
  "profile": {
    "mcp_server": "lune-research",
    "mcp_version": "3.0.1",
    "tool_count": 13,
    "tools": [
      {
        "name": "search_papers",
        "description": "Searches full-text, peer-reviewed papers from top-tier venues. Use for questions about academic papers, research topics, literature reviews, surveys, “what's been published on X”, named methods, or any claim that should be backed by a peer-reviewed citation. Returns each paper's metadata and the passages that matched. Hybrid semantic + lexical search over titles, abstracts, and full text. Phrase the query the way a researcher would describe the topic in prose, not a keyword bag. Triggering questions: “what's the latest on diffusion guidance”, “which papers compare against SimPO” (requirements "
      },
      {
        "name": "search_papers_many",
        "description": "Use for a LITERATURE SWEEP or survey: a research question broad enough to need several angles, e.g. \"what's been done on X\", a related-work section, or a state-of-the-field summary. One call returns one merged, ranked list of the papers the angles found, with the passages that matched. For a single focused question, use `search_papers` instead. Runs 1 to 25 query variants in ONE call and gets back a single deduped, RRF-merged ranked list with per-paper provenance (`matched_queries`: which of your queries, by index into the echoed `queries`, surfaced each paper, and at what rank): supply severa"
      },
      {
        "name": "get_paper_fulltext",
        "description": "Use this when the user asks “what does the methods/results section say”, wants to quote a specific section, or when the abstract isn't enough to verify a claim. Heavy: only call once a paper looks relevant from `search_papers`, `search_related_papers`, or `get_paper_citations`. `format=markdown` returns one rendered document; `format=json` returns a structured section list. Pass `sections` (case-insensitive headings, e.g. [\"Methods\"]) to fetch only those sections instead of the whole document."
      },
      {
        "name": "get_paper_citations",
        "description": "Use this when the user asks “what does this paper build on”, “what built on this”, traces influence chains, asks for follow-up work, or wants the lineage of an idea. `direction=cited_by` returns indexed papers that cite this one; `direction=cites` returns this paper's parsed references (which may or may not be in the corpus). Each edge carries `contexts`, the citing sentences with their section, which show HOW a work is used (baseline, method borrowed, result disputed). Page with `limit` / `offset`; the response reports `total` and `has_more` so you can walk a large citation set."
      },
      {
        "name": "list_conferences",
        "description": "Use this when the user asks “what conferences does Lune track”, “is venue X covered”, or wants a category-level browse (e.g. AI/ML, security, databases, software/systems). Pass `category` as a keyword (`ai`, `security`, ...) to narrow; it matches the conference's research area."
      },
      {
        "name": "get_conference_papers",
        "description": "Use this when the user asks for papers from a specific conference (optionally a year), e.g. “most-cited NeurIPS 2024 papers”, “show me CCS 2025 accepted papers”, or “what's new in security at IEEE S&P this year”. `sort` is `recency` (newest first, default) or `citations` (most-cited first); page with `limit` / `offset`."
      },
      {
        "name": "search_related_papers",
        "description": "Given a paper_id, return the most semantically similar papers by embedding distance, NOT by citation links. Use for “more papers like this one” / “adjacent work on the same topic”. For papers this one cites or that cite it, use `get_paper_citations` instead. Each hit carries metadata, abstract, the closest non-abstract matched chunk as `contexts`, and a `similarity` score (0..1, higher is nearer). Returns up to `limit` papers; an unknown paper_id is an error, an empty list means no neighbors were found."
      },
      {
        "name": "extract_from_papers",
        "description": "Pull a structured table out of up to 50 papers in ONE call: you define the columns (`fields`: each a snake_case `name`, a `type`, and an optional `description`) and an `instruction`, and the server reads each paper's full text and returns one typed row per paper. Use this when you need the SAME facts across many papers, e.g. \"dataset, model size, and reported accuracy for each of these papers\": one call reads every paper and returns the table. Pass `sections` (case-insensitive headings, e.g. [\"Results\"]) to focus extraction and cut noise. The model is instructed to use only what each paper sta"
      },
      {
        "name": "verify_claims",
        "description": "Fact-check 1 to 25 natural-language claims against Lune's peer-reviewed corpus in ONE call. For each claim the server retrieves the most relevant passages and an LLM judges the claim ONLY against those passages (never outside knowledge), returning one verdict per claim: `supported`, `unsupported`, or `insufficient_evidence`. Use this to ground a draft before you assert it, to vet a user's claim, or to check your own answer against the literature. Every verdict carries a `verbatim_quote` copied EXACTLY from a retrieved passage (or null when nothing could be quoted, e.g. an insufficient_evidence"
      },
      {
        "name": "gather_evidence",
        "description": "Use for a multi-part research task when you need to know whether your gathered evidence is SUFFICIENT, what is still MISSING, and what to search next, without the tool writing the answer. Pass the goal in `task` and your first search angles in `queries`; the server runs one corpus search per angle, decomposes the task into evidence requirements (or use your own via `requirements`), and returns each requirement as covered / partial / missing with the exact `evidence_spans` (verbatim quotes) that support it, plus `next_queries` for the gaps. Default `max_iterations=1` is a one-shot assessment bi"
      },
      {
        "name": "search_research_guidance",
        "description": "Use before recommending experimental design, ablation strategy, evaluation metrics, baselines, reproducibility, paper structure, related-work organisation, venue choice, response-to-reviewers, scientific writing, or methodology in general. Searches the Lune guidance corpus, curated from senior researchers, reproducibility checklists, venue-reviewer guidance, and author tutorials, so methodology advice can cite vetted sources. Triggering questions: “how should I design an ablation for X”, “what's a good evaluation setup for retrieval”, “how do I respond to reviewer 2”, “what's the reproducibili"
      },
      {
        "name": "get_research_guidance_doc",
        "description": "Use this AFTER `search_research_guidance` when you need the full text of a guidance document (not just the matched excerpt), for example to quote a passage or follow a checklist end-to-end. Pass the `doc_id` from a search hit."
      },
      {
        "name": "send_feedback",
        "description": "Sends the user's feedback about Lune to the Lune team. Use it when the user says something about Lune itself (papers it missed, results that did not fit, an error, something that helped, an idea): offer to pass it on, show the user the message you would send, and call this only after they agree. Set `allow_training` only if they also agreed that Lune may train models on it, after telling them they can withdraw that at any time in their Lune account settings. Write the message from what the user said about Lune, leaving out unrelated conversation and personal details."
      }
    ],
    "profiled_at": "2026-11-09T02:01:23.166Z"
  }
}