GoldenMatch

List stored Learning Memory corrections, optionally filtered by dataset. Returns id_a, id_b, decision, source, trust, reason, matchkey_name, dataset, original_score, created_at.

List available domain extraction rulebooks (built-in + user-defined).

List all registered goldenmatch plugins by category. Includes the 22 v1.18.2 predefined plugins (numeric/format/business/aggregation) plus any user-registered plugins via entry-points or PluginRegistry.register_*(). Each entry includes name, source (builtin or user), category, and the first line of the merge docstring.

List previous dedupe/match runs (for rollback) from the run log.

Alias for `match_record`. Match a single record against the loaded dataset in real-time. Paste a record's fields and instantly see if it matches any existing record. Uses the configured matchkeys, scorers, and thresholds. Example: {"name": "John Smith", "email": "john@test.com", "zip": "10001"}

Match a single record against the loaded dataset in real-time. Paste a record's fields and instantly see if it matches any existing record. Uses the configured matchkeys, scorers, and thresholds. Example: {"name": "John Smith", "email": "john@test.com", "zip": "10001"}

Return all corrections as a list of dicts (CSV-shaped). Caller is responsible for writing the file. Optionally filter by dataset.

Import corrections from a list of dicts (the exact shape memory_export returns). Upserts into the store: higher trust wins, same trust = latest wins. Returns the count imported.

Return Learning Memory status: total correction count, last learn time, and current learned adjustments. Cheap; safe for status checks.

Project per-stage distributed routing (scoring/clustering/golden) for a given data shape + cluster. Pure; no controller run.

Analyze the loaded dataset and recommend optimal PPRL (privacy-preserving record linkage) configuration. Returns recommended fields, bloom filter parameters, threshold, and explanation.

Run privacy-preserving record linkage between two parties' data. Computes bloom filters, matches records without sharing raw data. Specify fields, threshold, and security level.

Alias for `profile_data`. Get data quality profile: column types, null rates, unique counts, sample values.

Get data quality profile: column types, null rates, unique counts, sample values.

Semantic retrieval (#1089): return the records in a CSV most similar to a free-text query, ranked by cosine similarity. Embeds the chosen column and the query with the zero-config in-house embedder (no cloud/torch by default) and runs ANN search. The read side of the RAG entity-canonicalization epic -- fetch candidate records by query without running a full dedupe.

Run the config healer over the loaded dataset: analyze the dedupe run and return ranked, self-verified suggestions for improving the matching config (thresholds, scorers, negative evidence, blocking). Each suggestion carries an id, kind, target, rationale, and a machine-applicable patch. Requires the native kernel (pip install goldenmatch[native]); returns an empty list otherwise.

Undo a previous run by DELETING its output files (looked up by run_id in the run log). Destructive: removes the files that run wrote. Use list_runs first to find the run_id.

Run GoldenFlow data transforms on a CSV file. Normalizes phone numbers (E.164), dates (ISO), categorical spelling, and Unicode issues. Returns a manifest of transforms applied. Requires goldenflow: pip install goldenmatch[transform]

Run GoldenCheck data quality scan on a CSV file. Returns issues found (encoding errors, Unicode problems, format violations) without applying fixes. Requires goldencheck: pip install goldenmatch[quality]

Auto-map columns between two files with different schemas. Returns proposed (col_a, col_b) mappings with a confidence score and method (synonym / name_sim / composite). Useful before matching two sources.

Parameter-sensitivity analysis: sweep one or more config parameters across a range and report how stable the clustering is at each value (CCMS unchanged %). Use it to find robust thresholds. Auto-configures the file if no config is given.

Break an entire cluster into individual records. All members become singletons. Use when a cluster is completely wrong.

Analyze bad merges and suggest config changes. Provide examples of incorrect merges (pairs that should NOT have matched) and GoldenMatch will identify which fields/thresholds to tighten. Example: [{"record_a": {...}, "record_b": {...}, "reason": "different people"}]

Check if data needs privacy-preserving matching

Test a domain extraction rulebook against sample records. Shows what features would be extracted from the loaded data.

Remove a record from its cluster. The record becomes a singleton. Remaining cluster members are re-clustered using stored pair scores. Use this to fix bad merges.

Upload a local file's bytes to the server and get back a server-side path to reuse across other tools (analyze_data, auto_configure, agent_deduplicate, ...). No hosting needed. Send base64 (default) or raw text via `encoding`. Uploaded files are ephemeral scratch, reaped after GOLDENMATCH_MCP_UPLOAD_TTL (default 24h); re-upload if you need a path older than that. Max size GOLDENMATCH_MCP_MAX_UPLOAD_BYTES (default 64MB) -- above it, pass a public http(s) URL as file_path instead.