com.leap-labs/discovery-engine

MCPcommunity
v1.0.1com.leap-labsUnknownUpdated 2mo agoGitHub

Find novel, statistically validated patterns in tabular data — hypothesis-free.

Find novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss. Made by Leap Laboratories. Most data analysis starts with a question. Disco starts with the data. Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
2mo agoLast update
Package
Authorcom.leap-labs
LicenseUnknown
Version1.0.1
Sourcemcp-registry
Trust Status
B
60/100Good
Listed in Forge index+10/10
Publisher identity verified+0/25
Publisher: run `forge publish` from the package repo to claim ownership
Ed25519 publish signature+0/10
Included automatically when the publisher runs `forge publish`
Domain verification+0/5
Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
CVE scan · not run+0/30
Not yet scanned — package must be on npm
Static analysis · clean+20/20
npm provenance (Sigstore)+0/5
Publish from GitHub Actions with the --provenance flag
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain
Provenance
DependenciesNot audited
Tool surface
Security scan✓ CleanvHEAD · 19d ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

Find novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss. Made by Leap Laboratories. Most data analysis starts with a question. Disco starts with the data. Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like "patients aged 45–65 with low HDL and high CRP have 3× the readmission rate" — without you needing to…

Keywords
mcp