Find duplicate records in 30 seconds. Zero-config entity resolution, 97.2% F1 out of the box.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"goldenmatch": {
"url": "https://goldenmatch-mcp-production.up.railway.app/mcp/"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
A polyglot data-quality and entity-resolution toolkit. Polished, opinionated, AI-native.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'goldenmatch' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked goldenmatch against OSV.dev.
Click any tool to inspect its schema.
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in other
Pi Coding Agent extension (CLI-first) — routes bash/read/grep/find/ls through lean-ctx CLI for strong token savings. Optional MCP bridge can register advanced tools.
Autonomous spec-to-product coding-agent CLI with an MCP server exposing 34 tools over stdio.
97% token reduction for AI coding sessions — zero deps, 21 languages, MCP server
App framework, testing framework, and inspector for MCP Apps.
MCP Security Weekly
Get CVE alerts and security updates for io.github.benseverndev-oss/goldenmatch and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
A polyglot data-quality and entity-resolution toolkit. Polished, opinionated, AI-native.
GoldenCheck profiles → GoldenFlow standardizes → GoldenMatch deduplicates → GoldenAnalysis reports, all orchestrated by GoldenPipe. With InferMap for schema mapping, a Rust extension layer for Postgres / DuckDB, and optional WebAssembly acceleration behind the edge-safe TypeScript ports.
⚡ GoldenMatch scales from a CSV on your laptop to 100M+ rows on a Ray cluster — verified: 100,000,000 records deduped recall-complete (correct across any partitioning) in 9.2 min, with a 0.36 GB driver footprint.
Pair drilldown in the web workbench: cl