Read-only cross-cutting analysis, metrics, and reporting across the Golden Suite.
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π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Your customer data lives in a CRM, a billing system, and three spreadsheets nobody owns. Some records are duplicates. Some are the same company spelled four different ways. Nobody can answer how many customers do we actually have, and every dashboard built on top inherits the doubt.
Splink-beating entity resolution, Arrow-native and Rust-fast with zero tuning, feeding a durable identity layer so messy records from every source become stable golden entities with whole-record, Customer-360 provenance.
Zero-config matching that beats expert-tuned Splink head-to-head on messy customer records, in an Arrow-native, Rust-authoritative engine verified from a laptop CSV to a 250M-row dedupe in 11.2 minutes. The identities it produces live in a transaction-native control plane carrying stable entity_ids, per-field provenance, merge/split, and a tamper-evident audit log, all one call away as a Customer 360. It even owns its primitives: byte-identical, faster-than-rapidfuzz / jellyfish / FAISS Rust kernels, not rented dependencies.
Python Β· TypeScript Β· SQL, at 4-decimal parity Β· native in Postgres + DuckDB Β· edge WASM Β· 70+ MCP tools Β· beats hand-tuned Splink Β· 250M rows in 11.2 min
Pair drilldown in the web workbench: cluster members, field-level diff, and a one-line NL explanation per pair. pip install goldenmatch[web] then goldenmatch serve-ui <project>. More screenshots β
v3.17.1: The polars-free first run actually works now. 3.17.0 claimed this and did not deliver it: auto-config puts negative evidence on the exact matchkey by default, and that path still bridged to polars, so
goldenmatch dedupe customers.csvexited 3 on a default install. Verified the way it should have been the first time --pip installinto a clean polars-free venv, then the documented command.v3.17.0: The documented first run works on a default install.
pip install goldenmatchfollowed bygoldenmatch dedupe customers.csv-- the quickstart on every doc surface -- exited 3 on a polars-free install, which is what a plain install has produced since polars became an optional extra. Three separate polars imports on the zero-config path (auto-config ingest, the Arrow lane's preflight decline, and the csv writer) are gone, with polars' exact csv bytes reproduced and parity-pinned.v3.13.0: Fellegi-Sunter training runs distributed on Spark. The E-step reads only the comparison vector, so identical vectors collapse to one counted row and the whole step becomes a Spark
GROUP BYover agreement patterns -- the cluster counts, the driver only fits. Training cost tracks DISTINCT vectors (bounded byprod(levels + 1)), not pairs: 1M -> 5M rows grew candidate pairs 5.00x and the distributed counting stage 5.25x, while distinct patterns grew 3.0% (433 -> 446) and driver-side EM stayed at 0.01s. Runs on jar-only executors viagoldenmatch-spark, off the same Rust kernel every other surface uses.
Most entity-resolution tools hand you clusters and stop. GoldenMatch keeps going: it resolves messy records into a durable golden entity, one per real-world customer, that survives re-runs, carries provenance on every field, and answers "who is this, and where did each value come from?" in a single call.
entity_id (UUIDv7) that persists across runs as new data arrives. Records are absorbed, entities merge or split, but the id an entity earns is the id downstream systems can rely on. Run-local cluster numbers reshuffle on every run; these don't.customer_360(entity_id) composes it into one read: golden record, per-field provenance, every linked source record, the event timeline, and the entity's relationship neighborhood:
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