io.github.mightydatainc/ruc-mcp

MCPcommunity
v0.1.5io.github.mightydataincUnknownUpdated 1mo agoGitHub

An MCP server that fuses LLM contextual intelligence with procedural code reliability.

Render Unto Caesar (RUC) plugs into your AI agent and gives it the ability to write and run snippets of code on an as-needed basis — snippets of code that call right back to the AI agent during their execution. It effectively melds LLMs with traditional software, allowing each part of a task to be handled by the architecture that suits it best. The result is inference, judgment, and…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
1mo agoLast update
Package
Authorio.github.mightydatainc
LicenseUnknown
Version0.1.5
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

Render Unto Caesar (RUC) plugs into your AI agent and gives it the ability to write and run snippets of code on an as-needed basis &mdash; snippets of code that call right back to the AI agent during their execution. It effectively melds LLMs with traditional software, allowing each part of a task to be handled by the architecture that suits it best. The result is inference, judgment, and creativity that nonetheless executes methodically and reliably across long operations, large datasets, and…

Keywords
mcp