GoldenMatch

{
  "id": "aix_9e21c20f20",
  "name": "GoldenMatch",
  "operator": "(unclaimed - source: pulsemcp · publisher: github.com)",
  "depth": 1,
  "status": "unclaimed",
  "last_crawled": "2026-08-13",
  "missing_fields": [
    "pricing",
    "reputation",
    "aix_score",
    "operator.identity"
  ],
  "skills": [
    "general-tools"
  ],
  "protocols": {
    "mcp": "https://goldenmatch-mcp-production.up.railway.app/mcp/",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": null,
  "aix_score": null,
  "verification": {
    "identity": "none",
    "health": "liveness-only",
    "pricing": "unknown",
    "last_check": "2026-08-13T07:00:50.496Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://www.pulsemcp.com/servers/benzsevern-goldenmatch"
    },
    {
      "label": "repository",
      "url": "https://github.com/benseverndev-oss/goldenmatch"
    },
    {
      "label": "listing",
      "url": "https://github.com/benzsevern/goldenmatch.git"
    }
  ],
  "avatar": "https://github.com/benseverndev-oss.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/benseverndev-oss"
    }
  ],
  "public_stats": {
    "gh_stars": 125,
    "npm_downloads": 3498
  },
  "unreachable": false,
  "profile": {
    "mcp_server": "GoldenMatch",
    "mcp_version": "1.28.1",
    "tool_count": 77,
    "tools": [
      {
        "name": "analyze_data",
        "description": "Profile data, detect domain, recommend ER strategy"
      },
      {
        "name": "auto_configure",
        "description": "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`."
      },
      {
        "name": "controller_telemetry",
        "description": "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."
      },
      {
        "name": "agent_deduplicate",
        "description": "Run full ER pipeline with confidence gating and reasoning"
      },
      {
        "name": "agent_match_sources",
        "description": "Match two files with intelligent strategy selection"
      },
      {
        "name": "agent_explain_pair",
        "description": "Natural language explanation for a record pair"
      },
      {
        "name": "agent_explain_cluster",
        "description": "Explain why records are in the same cluster"
      },
      {
        "name": "agent_review_queue",
        "description": "Get borderline pairs awaiting approval"
      },
      {
        "name": "agent_approve_reject",
        "description": "Approve or reject a review queue pair"
      },
      {
        "name": "agent_compare_strategies",
        "description": "Compare ER strategies on your data"
      },
      {
        "name": "suggest_pprl",
        "description": "Check if data needs privacy-preserving matching"
      },
      {
        "name": "scan_quality",
        "description": "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]"
      },
      {
        "name": "fix_quality",
        "description": "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]"
      },
      {
        "name": "run_transforms",
        "description": "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]"
      },
      {
        "name": "sensitivity",
        "description": "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."
      },
      {
        "name": "incremental",
        "description": "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."
      },
      {
        "name": "certify_recall",
        "description": "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."
      },
      {
        "name": "retrieve_similar",
        "description": "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."
      },
      {
        "name": "upload_dataset",
        "description": "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."
      },
      {
        "name": "list_corrections",
        "description": "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."
      },
      {
        "name": "add_correction",
        "description": "Add a Learning Memory correction. Two shapes:\n  - pair-level: decision='approve' or 'reject', requires id_a + id_b\n  - field-level (v1.18.2+): decision='field_correct', requires cluster_id + field_name + corrected_value\nSource is 'agent' with trust=0.5 (lower than human steward 1.0). Pair (id_a, id_b) is canonicalized to (min, max) before storage."
      },
      {
        "name": "list_plugins",
        "description": "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."
      },
      {
        "name": "learn_thresholds",
        "description": "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."
      },
      {
        "name": "memory_stats",
        "description": "Return Learning Memory status: total correction count, last learn time, and current learned adjustments. Cheap; safe for status checks."
      },
      {
        "name": "memory_export",
        "description": "Return all corrections as a list of dicts (CSV-shaped). Caller is responsible for writing the file. Optionally filter by dataset."
      }
    ],
    "profiled_at": "2026-08-13T07:00:50.496Z"
  }
}