Lacuna Research MCP

🔬 Ground your coding agent in novel ideas, papers, and the ML landscape

Lacuna Research MCP gives AI researchers' coding agents:

• Novel research proposals. Explore novel research ideas generated with Alien Science.
• Research directions. Navigate concept clusters with linked papers, authors, and proposals.
• Agent-ready literature. Search recent papers in markdown with source links.
• Researcher intelligence. Trace authors, publications, directions, impact, and related researchers.
• Landscape mapping. Compare venues, institutions, leading researchers, and publication activity.

Lacuna, built by Tiptree Systems, is a research map of machine learning: a heterogeneous knowledge graph linking papers, research directions, authors, venues, institutions, and generated research proposals, with a source trail from every derived object back to the exact paper and page that produced it. Its pipeline reconciles scholarly records from OpenAlex, OpenReview, DBLP, and arXiv; extracts concept elements from paper text and clusters them into research directions (Lacuna paper); and samples novel research proposals from those directions with Alien Science. The map spans more than 730,000 papers, 190,000+ author profiles, 38,000+ research directions, and 3,000+ research proposals built from over 15 million concept elements — and it grows continuously as new arXiv and other AI papers are ingested.

Install · First use · Tools · API reference · Configuration

Install

The easiest way to install Lacuna Research MCP is to ask your coding agent, such as Codex or Claude Code:

Install and configure the lacuna-research-mcp package from PyPI for this client.

For manual setup, the instructions below use uvx to run the latest tagged release from PyPI. Install uv first; Lacuna Research MCP requires Python 3.11 or newer.

Codex

Add the server with the Codex CLI:

codex mcp add lacuna-research -- uvx lacuna-research-mcp

Alternatively, add the following to ~/.codex/config.toml (or to .codex/config.toml in a trusted project for project-only setup):

[mcp_servers.lacuna-research]
command = "uvx"
args = ["lacuna-research-mcp"]

Run codex mcp list to verify the server is configured. The Codex app, CLI, and IDE extension share this configuration on the same machine.

Claude Code

Add the server for all of your projects with the Claude Code CLI:

claude mcp add --scope user lacuna-research -- uvx lacuna-research-mcp

Omit --scope user to add it only to the current project. Alternatively, add the following under the top-level mcpServers object in ~/.claude.json:

{
  "mcpServers": {
    "lacuna-research": {
      "type": "stdio",
      "command": "uvx",
      "args": ["lacuna-research-mcp"]
    }
  }
}

Run claude mcp get lacuna-research to verify the server is configured.

Claude Desktop

Open Settings → Developer → Edit Config, then add the server under mcpServers in claude_desktop_config.json:

{
  "mcpServers": {
    "lacuna-research": {
      "command": "uvx",
      "args": ["lacuna-research-mcp"]
    }
  }
}

Restart Claude Desktop after saving the file.

Other MCP clients

For any client that supports local stdio MCP servers, use this standard configuration:

{
  "mcpServers": {
    "lacuna-research": {
      "command": "uvx",
      "args": ["lacuna-research-mcp"]
    }
  }
}

Standalone command

Install the MCP server as a persistent command:

uv tool install lacuna-research-mcp
lacuna-research-mcp

Run it without installing a persistent command:

uvx lacuna-research-mcp

Latest development version

PyPI contains tagged releases. To try the latest code from the main branch instead:

uvx --from git+https://github.com/tiptreesystems/lacuna-research-mcp.git lacuna-research-mcp

Local development

With uv:

git clone https://github.com/tiptreesystems/lacuna-research-mcp.git
cd lacuna-research-mcp
uv sync --extra dev
uv run lacuna-research-mcp

With pip:

cd <path-to-lacuna-research-mcp>
python3 -m venv .venv
. .venv/bin/activate
python -m pip install -U pip
python -m pip install -e .

First use

After connecting the server, call:

1. search_lacuna(query="LLM jailbreak defense", search_type="hypothesis", limit=10)
2. search_lacuna(query="methods for detecting prompt injection attacks", search_type="papers", limit=10) (production lexical+semantic paper ranking by default)
3. get_hypothesis(hypothesis_id_or_url="bd35de182c2325ae")
4. get_paper(artifact_id_or_url="art_79c57fbfec094f26b79c422cf08fed34") (defaults to view="context")
5. get_direction(cluster_id_or_url=25108) (defaults to view="context")

Scope

The corpus covers machine learning and AI research: papers, research directions, authors' research output, venues, institutions, and generated research hypotheses. It does not contain biographies, news, or non-research web content. Agents should answer questions outside that scope from other sources.

What it exposes

• search_lacuna Uses Lacuna's public /api/v1/search endpoint for directions, papers, authors, venues, institutions, and hypotheses. Explicit paper searches (search_type="paper") use the server's production lexical+semantic ranker when the other ranking arguments remain at their defaults. Pass search_type="hypothesis" (or "hypotheses" / "proposal" / "proposals") for hypothesis search.
• get_hypothesis(hypothesis_id_or_url, view="context") Hypothesis/proposal. view="context" (default) is a compact single-fetch proposal context (summary, abstract, linked directions); view="full" returns the server's version record with version history and signal counts. Proposal bodies are in versions[].markdown.
• get_direction(cluster_id_or_url, view="context") Research direction/cluster. view="context" (default) requests the compact agent-oriented summary; view="full" returns the raw cluster record.
• get_direction_papers(cluster_id_or_url, page, limit, view="compact") Paginated papers attached to a direction. view="compact" (default) returns citation-ready rows (id, url, title, year, venue, a few authors, abstract snippet); view="full" returns the raw upstream paper records.
• get_paper(artifact_id_or_url, view="context", figure_limit=None) Paper lookup. view="context" (default) requests the compact agent-oriented context; other views are "full", "preview", "blog", "figures", "concepts", or "neighbors". In context view, figure_limit caps the figure preview (server default 3; pass 0 to suppress previews while keeping a figures_truncated signal).
• Author tools: get_author_context(…, view="context"), get_author_papers, get_author_directions, and get_author_neighbors. Start with get_author_context, which defaults to the compact agent-oriented view (capped papers plus a readable impact_directions list instead of raw impact_clusters telemetry). Use the dedicated papers, directions, and neighbors tools to page through those collections without repeating the author context. view="full" returns the server-bounded full-shape context (collections remain capped at 100). Pass include_neighbors=true to explicitly include similar authors; this may add significant server latency.
• Venue and institution tools: get_venue_context(…, view="context"), get_institution_context(…, view="context"), get_institution_authors. Context tools default to compact (capped lists, duplicated blocks dropped; venue keeps a recent-activity slice that always includes the requested year). Use get_institution_authors to page through an institution's complete author list.

Wrapped APIs