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Explorer/MCP/benzsevern/goldenmatch
✓ REGISTRY VERIFIEDREMOTE

GoldenMatch

benzsevern/goldenmatch

Find and remove duplicate records from CSV files. Deduplicate customer lists, merge patient records, clean CRM data. Works with any CSV � auto-detects columns, no config needed. 30 tools: deduplicate, match across files, explain why records matched, fix bad merges, scan data quality, privacy-preserving linkage. 97.2% accuracy on standard benchmarks. Use from Claude to clean messy data in one conversation.

27 tools available
The Journeyman
A reasonable amount of history and nothing concerning in the scan.
Time indexed (5mo)
27toolsRemote/ HTTP5moindexed
100% uptime · 385ms avgChecked Aug 6, 2026
Quality Score
63/95
Deep
Risk Score
0/100
Clean
How is this calculated?
Quality Breakdown
Tenure13.6/20
142 days indexed
Capability19.4/25
Tools: 7.4/13 (27 tools)
Description: 5/5
Endpoint: 7/7
Adoption4.8/25
Use count: 4.8/20 (15 uses)
Multi-registry: 0/5 (1 registry)
Reliability25/25
Currently live: 10/10
Uptime history: 15/15 100% (51/51 checks)
Security scan: 0 pts in v1.0; ready to weight when coverage improves
Risk
0Clean
No signals detected.
The scanner shows
27 tools. Registry verified. Nothing caught our attention.
First indexed Mar 27, 2026
Server Profile
Tools catalogued
27
27 tools available. Full list below.
Hosting
Remote / HTTP
Runs on the internet. No access to your filesystem, SSH keys, or environment variables.
Registry presence
Verified
Listed on the Official MCP Registry under Linux Foundation governance.
Liveness
100%
Based on 48 checks. Average response: 385ms.
Publisher Verification
✓ Listed on the Official MCP Registry under Linux Foundation governance.
Endpoint
https://goldenmatch--benzsevern.run.tools
Tools (27)
analyze_data
Profile data, detect domain, recommend ER strategy
auto_configure
Generate optimal matching config from data analysis
agent_deduplicate
Run full ER pipeline with confidence gating and reasoning
agent_match_sources
Match two files with intelligent strategy selection
agent_explain_pair
Natural language explanation for a record pair
agent_explain_cluster
Explain why records are in the same cluster
Show all 27 tools ↓
agent_review_queue
Get borderline pairs awaiting approval
agent_approve_reject
Approve or reject a review queue pair
agent_compare_strategies
Compare ER strategies on your data
suggest_pprl
Check if data needs privacy-preserving matching
get_stats
Get dataset statistics: record count, cluster count, match rate, cluster sizes.
find_duplicates
Find duplicate matches for a record. Provide field values to search against the loaded dataset.
explain_match
Explain why two records match or don't match. Shows per-field score breakdown.
list_clusters
List duplicate clusters found in the dataset. Returns cluster IDs, sizes, and member counts.
get_cluster
Get details of a specific cluster: all member records and their field values.
get_golden_record
Get the merged golden (canonical) record for a cluster.
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"}
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.
shatter_cluster
Break an entire cluster into individual records. All members become singletons. Use when a cluster is completely wrong.
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"}]
profile_data
Get data quality profile: column types, null rates, unique counts, sample values.
export_results
Export matching results to a file (CSV or JSON).
list_domains
List available domain extraction rulebooks (built-in + user-defined).
create_domain
Create a custom domain extraction rulebook. Define patterns for a specific data domain (medical devices, automotive parts, real estate, etc.).
test_domain
Test a domain extraction rulebook against sample records. Shows what features would be extracted from the loaded data.
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.

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