io.github.pablixnieto2/etld-mcp-server

MCPcommunity
v3.2.2io.github.pablixnieto2UnknownUpdated 3mo agonpmGitHub

Deterministic B2B Data Middleware. Waterfall parsing for CSV, EDI, SEC & Finance. No hallucinations.

ETL-D is a deterministic data middleware designed to act as a shield for AI Agents. It stops LLMs from "hallucinating" over structured data by providing a strict, 3-layer parsing architecture via the Model Context Protocol (MCP). Standard LLMs are terrible at reading raw B2B files (CSV, PDF, EDI, Norma 43). They suffer from: 1. Token Exhaustion: Sending a 5,000-row CSV to context is a waste of…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
3mo agoLast update
Package
Authorio.github.pablixnieto2
LicenseUnknown
Version3.2.2
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 · 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
Dependencies60 resolved+ · none vulnerable
Tool surface
Security scan✓ Cleanv3.3.3 · 20d 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

ETL-D is a deterministic data middleware designed to act as a shield for AI Agents. It stops LLMs from "hallucinating" over structured data by providing a strict, 3-layer parsing architecture via the Model Context Protocol (MCP). Standard LLMs are terrible at reading raw B2B files (CSV, PDF, EDI, Norma 43). They suffer from: 1. Token Exhaustion: Sending a 5,000-row CSV to context is a waste of money. 2. Precision Loss: LLMs struggle with spatial alignment. A misplaced comma in a bank statement…

Keywords
mcp