Skip to main content
Glama

search_lacuna

Read-onlyIdempotent

Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, and novel hypotheses. For new research ideas, use the 'hypothesis' search type.

Instructions

Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, and novel research hypotheses.

For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results.

search_type accepts all, cluster/direction, paper, author, institution, venue, or hypothesis/proposal; singular and plural aliases are accepted. Use other sources for biographies, news, and non-research web content.

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.

fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue. Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNorelevance
limitNo
queryYes
venueNo
fieldsNo
offsetNo
date_toNo
date_fromNo
search_typeNoall
ranking_profileNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses important behavioral details beyond annotations: ranking profiles (lexical vs semantic), restrictions on combining semantic ranking with year sorting, inclusive date format constraints, and field weighting syntax. It also explains that fields must exist on the selected search_type. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured in clear short paragraphs, each addressing a specific parameter group. It is dense but every sentence provides necessary operational detail, and the front-loaded main purpose ensures quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 10 parameters and zero schema descriptions, the description covers all parameter semantics and usage constraints. The existence of an output schema means return format need not be in the description. This is a complete and self-sufficient description for correct tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description fully compensates by explaining every parameter: search_type aliases, ranking_profile options, sort values, date formats, and fields syntax with examples. This is essential context that the schema lacks, making the description highly valuable for correct invocations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches Lacuna's ML/AI corpus for multiple entity types (papers, directions, authors, venues, institutions, hypotheses). It distinguishes itself from sibling tools like get_hypothesis and get_paper by focusing on search rather than retrieval of specific entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided: use search_type='hypothesis' for novel research ideas and then call get_hypothesis. It also instructs to use other sources for biographies, news, and non-research content, clearly delimiting when this tool is appropriate versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tiptreesystems/lacuna-research-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server