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Lacuna Research MCP Server

answering

Lacuna Research MCP is answering right now. Last checked 10 min ago. 1 186 installs a week from pypi. It exposes 13 tools. Last commit 1 Nov 2026.

Search Lacuna's ML research graph for papers, directions, authors, venues, and hypotheses.

Installs per day peak 965 · avg 320 · -56% w/w
a month agotoday
Uptime history 21 days of history · worst day 97%
21 days agonow
98.9%
Uptime 24h
90 of 91 checks
13
Tools
read from the server
413 ms
Response time
average over 24h
1 186
Installs / week
npm and PyPI

What changed 1

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 15 October 2026. No other catalogue keeps this.

15 Oct a tool changed version

Lacuna Research MCP missed one check this week

Everything else answered, so this is steady rather than shaky. We check every 15 minutes, which is how a one-off gets told apart from the start of a pattern, and how you hear about the next one within the hour instead of from your users.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 10 min ago.

run in your terminal
claude mcp add lacuna-research-mcp --transport http https://lacuna.tiptreesystems.com/api/mcp
Settings → Connectors → Add custom connector, then paste this address
https://lacuna.tiptreesystems.com/api/mcp
~/.codex/config.toml
[mcp_servers.lacuna-research-mcp]
url = "https://lacuna.tiptreesystems.com/api/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "lacuna-research-mcp": {
      "url": "https://lacuna.tiptreesystems.com/api/mcp"
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "lacuna-research-mcp": {
      "type": "http",
      "url": "https://lacuna.tiptreesystems.com/api/mcp"
    }
  }
}

Available tools 13

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

author
get_author_context
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 A free-text `affiliation` field may be present but can be incomplete or outdated and may not represent current employment; the corpus has no biography or employment history, so do not infer those from this data.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.
get_author_directions
Fetch one page of an author's named research directions.
get_author_neighbors
Fetch one ranked page of neighboring/similar Lacuna authors.
get_author_papers
Fetch one page of an author's papers, ordered from newest to oldest.
direction
get_direction
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.
get_direction_papers
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.).
institution
get_institution_authors
Fetch one page of authors affiliated with a Lacuna institution. Results are ordered by paper count. Use authors_total, authors_returned, authors_offset, and authors_truncated to page through the complete list.
get_institution_context
Fetch agent-oriented context for a Lacuna institution. 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.
hypothesis
get_hypothesis
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.
lacuna
search_lacuna
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.
paper
get_paper
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.
resource
get_resource
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.
venue
get_venue_context
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).

Endpoints

URLTransportStateLatencyChecked
https://lacuna.tiptreesystems.com/api/mcp streamable-http answering 189 ms 10 min ago

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Lacuna Research MCP — questions

Answers built from our own checks of this server.

What can Lacuna Research MCP do?
It exposes 13 tools, read directly from the server on our last check. Among them: get_author_context, get_author_directions, get_author_neighbors, get_author_papers, get_direction, get_direction_papers and 7 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is Lacuna Research MCP mostly used for?
Its tools cluster around author, direction and institution. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is Lacuna Research MCP working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 90 of 91 checks got a reply (98.9%), average response time 413 ms. The bar chart above shows every period we have measured.
How do I connect Lacuna Research MCP?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Lacuna Research MCP need an API key?
No. Lacuna Research MCP completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 13 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Lacuna Research MCP?
It answers our handshake in 413 ms on average, which is faster than 31% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use Lacuna Research MCP?
The pypi package lacuna-research-mcp was installed 1 186 times in the last week. Week over week that is -56%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is Lacuna Research MCP open source?
The code is open source: it is published under the MIT licence, written in Python, 47 stars on GitHub and 1 open issue. The licence covers the code in the repository, not the hosted service at lacuna.tiptreesystems.com, which runs on its operator's own terms.