GoldenMatch MCP Server

by benseverndev-oss Your server? Claim it

answering

GoldenMatch is answering right now. Last checked 1 min ago. 1 503 installs a week from pypi. It exposes 77 tools. Last commit 18 Sep 2026.

Find duplicate records in 30 seconds. Zero-config entity resolution, 97.2% F1 out of the box.

Installs per day peak 857 · avg 297 · +4% w/w

a month agotoday

Uptime history 47 days of history · worst day 99%

47 days agonow

100.0%

Uptime 24h

92 of 92 checks

Tools

read from the server

358 ms

Response time

average over 24h

1 503

Installs / week

npm and PyPI

What the code does

We read the source, 23 h ago · tools taken from the live server · rules 3dff92dd89df

A tool parameter reaches a dangerous call

A value the model can set ends up inside a file or shell call. That is not a flaw by itself — for a terminal server it is the job — but it is where things go wrong when it is not.

A tool parameter reaches a file or shell call write_csv.path → packages/rust/extensions/native-flow/src/csvio.rs:284

        std::fs::File::create(path).map_err(|e| format!("create {path}: {e}"))?,

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

A line of code looks encrypted packages/typescript/goldencheck/src/core/_wasm/goldencheckWasmBytes.ts:5, packages/typescript/goldenmatch/src/core/_wasm/documentsWasmBytes.ts:5, packages/typescript/goldenmatch/src/core/_wasm/fingerprintWasmBytes.ts:5 и ещё 2

  "AGFzbQEAAAAB8gEiYAAAYAABf2ABfwBgAX8Bf2ABfwF+YAJ/fwBgAn9/AX9gAn9/BH9/f39gAn9/AX5gAn9/AW9gA39/fwBgA39/fwF/YAR/f39/AGAEf39/fwF/YAR/f39/BH9/f39gBX9/f39/AGAFf39/f38Bf2AFf39/f38Ef39/f2AGf39/f39/AGAGf39/f39/AX9gB39/f39/f38AYAR/f39+AGAFf39/fn8AYAR/f398AGADf39+AGAEf39+fgBgA39/fAR/f39/Y…

File ships in the package but is absent from the source [пакет] .devcontainer/post-create.sh:1, [пакет] deploy/sail-gke/diag.py:1, [пакет] deploy/sail-gke/gen_sail_smoke.py:1 и ещё 165

этот файл ставится пользователю, но в репозитории его нет

Builds a database query by concatenation docs-site/goldenanalysis/cross-run.mdx:40, packages/python/goldengraph/goldengraph/synthesize.py:186

The worked example: a per-metric **2% gate on `match.recall_safe_bound`** catches a 0.97 → 0.89 recall drop that a global 10% gate would miss, exactly the kind of quiet quality regression a nightly pipeline should alert on.

Runs an external command packages/python/golden-suite/golden_suite/cli.py:147, packages/python/goldencheck/goldencheck/cli/main.py:442, packages/python/goldenmatch/goldenmatch/core/er_matcher/serve_local.py:66 и ещё 1

            result = subprocess.run(cmd, check=False)

Builds a file path from a variable packages/python/goldenmatch/deploy/sail-gke/gen_sail_smoke.py:128, packages/python/goldenmatch/goldenmatch/core/_llm_loader.py:137, [пакет] deploy/sail-gke/gen_sail_smoke.py:128 и ещё 2

            sub.write_parquet(os.path.join(out, f"part-{k:02d}.parquet"))

Executes code built at runtime packages/python/goldenmatch/goldenmatch/core/diagnostics.py:51, [пакет] goldenmatch/core/diagnostics.py:51

            m = __import__(mod, fromlist=[name])

Long encoded blob in source packages/rust/extensions/goldenembed-core/golden/project_golden.json:1, packages/typescript/goldencheck/src/core/_wasm/goldencheckWasmBytes.ts:5, packages/typescript/goldenmatch/src/core/_wasm/clusterWasmBytes.ts:5 и ещё 11

… "ngram_min": 2, "ngram_max": 4, "lowercase": true, "boundary": "\u0002", "seed": 0}, "n_features": 128, "weights_b64": "bL+APH9Gh7y98qM939VWPKEhib39Ijk9eekmPuZz8j3+J7S9VfkhvuqOn71uRak7Ts2UvuwK4LwDeh++4HW7vY9Ui70Y8iG9PcFSPRRxBT6XnoO8RuguPjNKqr0h+TM90knnPYWJQDz4Vb69MvbrvQpbar3VeuE…

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

A tool parameter here reaches a dangerous call

That is not a flaw by itself — but it is where things go wrong when it is not the job. We re-read this code on every release. Watch it and you hear from us the day another one appears.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 1 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add goldenmatch --transport http https://goldenmatch-mcp-production.up.railway.app/mcp/

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "goldenmatch": {
      "url": "https://goldenmatch-mcp-production.up.railway.app/mcp/"
    }
  }
}

~/.codex/config.toml

[mcp_servers.goldenmatch]
url = "https://goldenmatch-mcp-production.up.railway.app/mcp/"

.cursor/mcp.json

{
  "mcpServers": {
    "goldenmatch": {
      "url": "https://goldenmatch-mcp-production.up.railway.app/mcp/"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "goldenmatch": {
      "url": "https://goldenmatch-mcp-production.up.railway.app/mcp/"
    }
  }
}

This endpoint answered with an authorization challenge. The server is running, but it did not say what kind of credentials it expects.

Available tools 77

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

identity

identity_audit

Export the append-only identity audit log in commit order: every event with actor / trust / timestamp / reason, so a reviewer can reconstruct exactly which actor changed what, when, and why. Optionally filtered by dataset / actor.

identity_audit_seal

Anchor the append-only audit log with a tamper-evidence seal: a chained sha256 root over every event since the last seal. Cheap and idempotent (a no-op when nothing new has been logged). Run it periodically (or after a batch of stewardship actions) so the history becomes provably untampered. Optionally scoped to a dataset. Publish/mirror the returned root_hash to make tampering detectable by an external party.

identity_audit_verify

Verify the append-only audit log against its seal chain. Replays the per-event content hashes and the seal roots to detect content edits, deletion, reordering, and insertion of any sealed event. Returns {ok, events_checked, seals_checked} plus the ids of any content mismatches / broken seals / missing sealed events. Optionally scoped to a dataset.

identity_claim

Claim a record into an identity, moving it out of any prior entity ('this record belongs to that identity'). Emits a provenance-stamped `claimed` event on both the gaining and losing entities.

identity_conflicts

List evidence edges marked `conflicts_with`.

identity_history

Return the temporal event log for an identity.

identity_list

List identities, optionally filtered by dataset/status.

identity_merge

Manually merge two identities. All records from `absorb_entity_id` are reassigned to `keep_entity_id`. The merge events are stamped with `actor`/`trust` provenance so the audit log records who merged these and on what authority.

identity_profile

MDM profile of one entity: record count + per-source breakdown, golden record, confidence, conflict count, canonical version (structural-event count), and first/last activity. Returns {found: false} when no such entity exists.

identity_resolve

Resolve a record_id to its durable identity. Returns the full identity view (members, evidence edges, recent events) or null when no identity exists for that record.

identity_resolve_conflict

Adjudicate a `conflicts_with` pair: 'same' keeps the entity intact, 'distinct' splits the second record out into a new identity, 'defer' only logs. Records a durable mediation verdict + event with actor/trust provenance, and stops the conflict re-surfacing in the open-conflicts queue.

identity_show

Fetch the full detail of one identity by entity_id: its member records, evidence edges, and recent event log. Returns {found: false} when no such entity exists.

identity_split

Split a subset of records off an identity into a brand-new identity. The original keeps the remaining records. The split events carry `actor`/`trust` provenance.

identity_stats

Graph-level summary / health stats: entities by status, total records, records-per-entity distribution, conflict total, source mix, and the largest entities. Optionally scoped to a dataset.

identity_worklist

Prioritized steward worklist: active entities needing attention (open conflicts and/or confidence below weak_confidence), highest conflict count first.

agent

agent_approve_reject

Approve or reject a review queue pair

agent_compare_strategies

Compare ER strategies on your data

agent_deduplicate

Run full ER pipeline with confidence gating and reasoning

agent_explain_cluster

Explain why records are in the same cluster

agent_explain_pair

Natural language explanation for a record pair

agent_match_sources

Match two files with intelligent strategy selection

agent_review_queue

Get borderline pairs awaiting approval

explain

explain_cluster

Alias for `agent_explain_cluster`. Explain why records are in the same cluster

explain_match

Explain why two records match or don't match. Shows per-field score breakdown.

explain_pair

Alias for `explain_match`. Explain why two records match or don't match. Shows per-field score breakdown.

explain_routing

Human-readable explanation of why each stage is routed the way it is, with the driver-RAM projection that drove it.

memory

memory_export

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

memory_import

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.

memory_stats

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

analyze

analyze_blocking

Diagnose blocking on the loaded dataset: returns ranked blocking key candidates with block counts, max block size, total candidate comparisons, and estimated recall. Use it to explain why matching is slow or produces too many candidate pairs.

analyze_data

Profile data, detect domain, recommend ER strategy

documents

documents_ingest

Extract records from documents (PDF/image) against a target schema into rows ready for dedupe_df. Returns records + an ingest report.

documents_suggest_schema

Propose a target extraction schema (JSON) from a sample document image/PDF.

match

match

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": "[email protected]", "zip": "10001"}

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": "[email protected]", "zip": "10001"}

pprl

pprl_auto_config

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

pprl_link

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

profile

profile

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

profile_data

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

suggest

suggest_config

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"}]

suggest_pprl

Check if data needs privacy-preserving matching

auto

auto_configure

Run AutoConfigController on a CSV; return the committed GoldenMatchConfig (incl. negative_evidence / Path Y when chosen) plus telemetry — stop_reason, health, decision trace, indicator column priors. Programmatic equivalent of `goldenmatch autoconfig`.

certify

certify_recall

Estimate match RECALL without ground truth (unsupervised). Treats each auto-configured matchkey/pass as a decorrelated system and uses capture-recapture over their overlaps to estimate how many true matches were missed. Returns a point estimate (a safe lower bound additionally needs a small labelled audit; see `goldenmatch evaluate --certify --audit-out`). Needs >=3 decorrelated systems.

cluster

get_cluster

Get details of a specific cluster: all member records and their field values.

clusters

list_clusters

List duplicate clusters found in the dataset. Returns cluster IDs, sizes, and member counts.

compare

compare_clusters

Compare two ER clustering outcomes on the same dataset without ground truth (CCMS): classifies each cluster as unchanged / merged / partitioned / overlapping and returns the Talburt-Wang Index. Both inputs are JSON cluster files (as written by export-style output).

config

config_weaknesses

Diagnose weaknesses in the loaded run's auto-config: columns admitted that shouldn't be (source/provenance labels, per-row IDs), oversized or shared-value blocks, null sinks, low-signal matchkeys, and over-merging. Returns ranked findings, each with a plain-English explanation + a concrete fix, plus a one-paragraph summary.

controller

controller_telemetry

Return the AutoConfigController telemetry from the most recent `auto_configure` or `agent_deduplicate` call in this MCP session. Same JSON shape as the web /api/v1/controller/telemetry endpoint.

correction

add_correction

Add a Learning Memory correction. Two shapes: - pair-level: decision='approve' or 'reject', requires id_a + id_b - field-level (v1.18.2+): decision='field_correct', requires cluster_id + field_name + corrected_value Source is 'agent' with trust=0.5 (lower than human steward 1.0). Pair (id_a, id_b) is canonicalized to (min, max) before storage.

corrections

list_corrections

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.

dedupe

dedupe

Alias for `find_duplicates`. Find duplicate matches for a record. Provide field values to search against the loaded dataset.

domain

create_domain

Create a custom domain extraction rulebook. Define patterns for a specific data domain (medical devices, automotive parts, real estate, etc.).

domains

list_domains

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

duplicates

find_duplicates

Find duplicate matches for a record. Provide field values to search against the loaded dataset.

evaluate

evaluate

Score the loaded run against ground-truth pairs. Loads a ground-truth CSV (id_a,id_b columns) and returns precision, recall, and F1 for the current clustering.

export

export_results

Export matching results to a file (CSV or JSON).

fix

fix_quality

Run GoldenCheck scan and apply fixes to a CSV file. Returns the fixed data summary and a manifest of all fixes applied. Requires goldencheck: pip install goldenmatch[quality]

golden

get_golden_record

Get the merged golden (canonical) record for a cluster.

incremental

incremental

Match a batch of new records against an existing base dataset (without re-running the whole base). Returns matched (new_row_id, base_row_id, score) pairs plus counts. Auto-configures from the base file if no config is given.

learn

learn_thresholds

Force a MemoryLearner pass over accumulated corrections. Returns the list of LearnedAdjustments produced (matchkey_name, threshold, sample_size, learned_at). Requires >= 10 corrections per matchkey before threshold tuning fires; otherwise returns an empty list.

lineage

lineage

Field-level provenance for the loaded run: for each scored pair, the per-field scores that produced the match, plus cluster id. Optionally write a lineage JSON to a directory.

lint

lint_routing

Flag config/env overrides that force a slow path (e.g. CLUSTERING_THRESHOLD=0 when the edge set fits driver RAM). ERROR at scale; would_refuse mirrors the runtime guard.

plan

plan_routing

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

plugins

list_plugins

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.

retrieve

retrieve_similar

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.

review

review_config

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.

rollback

rollback

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.

runs

list_runs

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

scan

scan_quality

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]

schema

schema_match

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.

sensitivity

sensitivity

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.

shatter

shatter_cluster

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

stats

get_stats

Get dataset statistics: record count, cluster count, match rate, cluster sizes.

test

test_domain

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

transforms

run_transforms

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]

unmerge

unmerge_record

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

upload_dataset

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.

Endpoints

URL	Transport	State	Latency	Checked
https://goldenmatch-mcp-production.up.railway.app/mcp/	streamable-http	needs auth	324 ms	1 min ago

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GoldenMatch — questions

Answers built from our own checks of this server.

What can GoldenMatch do?

It exposes 77 tools, read directly from the server on our last check. Among them: add_correction, agent_approve_reject, agent_compare_strategies, agent_deduplicate, agent_explain_cluster, agent_explain_pair and 71 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

What is GoldenMatch mostly used for?

Its tools cluster around identity, agent and explain. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.

Is GoldenMatch working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 358 ms. The bar chart above shows every period we have measured.

How do I connect GoldenMatch?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address. You will need your own credentials: it refuses anonymous clients.

Does GoldenMatch need an API key?

Yes. Every time we knock, GoldenMatch answers with an authorization challenge instead of its tool list — that is how we know it is running and gated rather than broken. Bring your own credentials and it will talk.

How fast is GoldenMatch?

It answers our handshake in 358 ms on average, which is faster than 43% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.

How many people use GoldenMatch?

The pypi package goldenmatch was installed 1 503 times in the last week. Week over week that is +4%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is GoldenMatch open source?

Yes — it is published under the MIT licence, written in Python, 132 stars on GitHub and 3 open issues. The source link is on this page, so you can read exactly what it does with your data before you connect it.