Lacuna Research MCP

Fetch agent-oriented context for a Lacuna author. Author profiles describe research output (papers, directions, impact). A free-text `affiliation` field may be present but can be incomplete or outdated and may not represent current employment; the corpus has no biography or employment history, so do not infer those from this data. view selects the response shape: - "context" (default, recommended): Lacuna's compact author context — capped readable papers and an `impact_directions` list (named research directions) in place of the raw numeric `impact_clusters` telemetry, with the duplicated nested author record dropped server-side. - "full": the bounded full-shape author context (raw `impact_clusters`, nested author record; server collections are capped at 100). Set include_neighbors=True to include similar authors as named, linkable records. Neighbor computation may add significant server latency.

Fetch one page of an author's named research directions.

Fetch one ranked page of neighboring/similar Lacuna authors.

Fetch one page of an author's papers, ordered from newest to oldest.

Fetch a Lacuna research direction/cluster. Use a numeric cluster ID (for example 25005) or a direction URL ending in that number. view selects the response shape (`context` typically contains the fields `full` provides plus the agent-oriented summary content): - "context" (default, recommended): compact agent-oriented summary with summary_markdown, capped papers/authors/related_directions, and truncation markers. - "full": raw upstream cluster record only. Cheaper than context when you only need basic cluster metadata.

Fetch paginated papers associated with a Lacuna research direction/cluster. view selects the per-paper shape: - "compact" (default, recommended): citation-ready rows (id, url, title, year, venue, a few authors, abstract snippet). Drops the raw upstream info blob and levels.cluster internals that otherwise dominate the payload. - "full": the complete upstream paper records. Much larger; use only when you need the raw metadata (openalex/dblp/arxiv ids, etc.).

Fetch a generated novel ML/AI research proposal from Lacuna. Use after search_lacuna(search_type="hypothesis") to inspect a proposal. view selects the response shape: - "context" (default, recommended): compact single-fetch proposal context — summary_markdown, abstract, and linked directions, with the raw upstream record (whose markdown duplicates summary_markdown) dropped server-side. - "full": the server's version record, including version history and signal counts. Proposal bodies are in versions[].markdown. Use only when you need versions or signals.

Fetch one page of authors who list a Lacuna institution on their papers. Each author appears because published papers list this institution as one of their affiliations. This is a record of research output, not a staff directory: it may be incomplete or out of date and does not show current employment. Each entry has the author's name, Lacuna page and paper count; there are no contact details or other personal data. Results are ordered by paper count. Use authors_total, authors_returned, authors_offset, and authors_truncated to page through the complete list.

Fetch agent-oriented context for a Lacuna institution (a university, lab or company). The context summarizes the institution's research output: its paper and author counts and the authors who publish most often under it. Affiliations come from the author affiliations listed on published papers, not from staff directories, and may be incomplete or out of date. The response has no contact details, biographies or employment records. view selects the response shape: - "context" (default, recommended): compact institution context — capped top authors with the duplicated institution block dropped server-side. - "full": the complete institution context shape (duplicated institution record and a bounded author list with explicit truncation metadata). Use get_institution_authors to page through the complete author list.

Fetch a Lacuna paper by artifact id or paper URL. include_resources adds linked resources (code repositories, datasets, models, and demos) in context and full views by default. Pass False to omit them; other views ignore this option. Pass a resource entry's id to get_resource for further details. view selects the response shape. `context` requests Lacuna's compact agent-oriented context by default, while the four single-field views (blog/figures/concepts/neighbors) return isolated sub-resources: - "context" (default, recommended): agent-oriented summary with summary_markdown, authors, and a small figure preview. Start here for almost everything. Available versions are included when present; to read another version, use its artifact ID from the returned version list. - "full": raw upstream paper record. Cheaper than context when you only need basic metadata. - "preview": compact card with unique fields `excerpt`, `excerpt_kind`, `bookmarked`. Use for citation-style display. - "blog": just the summary_markdown content, without the rest of the context envelope. - "figures": just the figures list. - "concepts": just the concepts list. - "neighbors": just the related-papers list. figure_limit (context view only) caps the figure preview (server default 3). Pass 0 to suppress figure previews while keeping a `figures_truncated` signal.

Fetch a Lacuna research resource (code repository, dataset, model, or demo). Accepts a resource artifact ID (art_...) or a Lacuna resource URL containing that ID (/resource/<slug>/art_...). The response includes the external url, a summary, facets (tasks, modalities, size, license, access), provider metrics, a README/card excerpt, `publications` (linked papers with paper_id, title, venue, year, and relationship such as dataset_for or code_for), related research directions, and other resources mentioned by this one. Pass a publication's paper_id to get_paper to read the paper and its reported results.

Fetch agent-oriented context for a Lacuna venue, optionally scoped to a year. Venue keys are opaque hashes (e.g. "d7bf22905bd6"), never human-readable names like "icml". Find the key first via search_lacuna(search_type="venue") or pass a /venue/... page URL. view selects the response shape: - "context" (default, recommended): compact venue context — capped top authors, non-placeholder top clusters, and a recent-activity slice (the requested `year` is always included), with the duplicated venue block and full year histogram dropped server-side. - "full": the complete venue context (full year histogram, all top authors/ clusters, duplicated venue record).

Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.