ref-tools-mcp

MCPcommunity
v3.0.3UnknownUnknownUpdated 8mo agonpm

ModelContextProtocol server for Ref

A ModelContextProtocol server that gives your AI coding tool or agent access to documentation for APIs, services, libraries etc. It's your one-stop-shop to keep your agent up-to-date on documentation in a fast and token-efficient way. For more see info ref.tools Ref's tools are design to match how models search while using as little context as possible to reduce context ro

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
689Downloads/wk
8mo agoLast update
Package
AuthorUnknown
LicenseUnknown
Version3.0.3
Sourcenpm
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 · clean+30/30
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✓ Cleanv3.0.3 · 1mo ago
EvalsNone
IndexedMay 24, 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

![Documentation for your agent](https://ref.tools) ![smithery badge](https://smithery.ai/server/@ref-tools/ref-tools-mcp) A ModelContextProtocol server that gives your AI coding tool or agent access to documentation for APIs, services, libraries etc. It's your one-stop-shop to keep your agent up-to-date on documentation in a fast and token-efficient way. For more see info ref.tools Ref's tools are design to match how models search while using as little context as possible to reduce context ro