B

goldenmatch-mcp-production.up.railway.app

https://goldenmatch-mcp-production.up.railway.app/mcp/
86/100 · MCP Trust Grade · checked 4h ago · MCP 2025-06-18
✓ Watched since 2026-06-03 — behavioral baseline locked. We re-check this server's tool surface on a schedule; if it adds, removes, or silently rewrites a tool (rug-pull), we record it.

What it offers — 42 tools · Developer Tools

analyze_data

Profile data, detect domain, recommend ER strategy

auto_configure

Run AutoConfigController on a CSV; return the committed GoldenMatchConfig (incl. negative_evidence / Path Y when chosen) plus telemetry — stop_reason,

controller_telemetry

Return the AutoConfigController telemetry from the most recent `auto_configure` or `agent_deduplicate` call in this MCP session. Same JSON shape as th

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

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

scan_quality

Run GoldenCheck data quality scan on a CSV file. Returns issues found (encoding errors, Unicode problems, format violations) without applying fixes. R

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 inst

run_transforms

Run GoldenFlow data transforms on a CSV file. Normalizes phone numbers (E.164), dates (ISO), categorical spelling, and Unicode issues. Returns a manif

list_corrections

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

add_correction

Add a pair correction to Learning Memory. Source is set to 'agent' with trust=0.5 (lower than human steward decisions which are 1.0). Pair (id_a, id_b

learn_thresholds

Force a MemoryLearner pass over accumulated corrections. Returns the list of LearnedAdjustments produced (matchkey_name, threshold, sample_size, learn

memory_stats

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

+24 more tools

Spec conformance20%80
Security (OWASP MCP)30%100
Reliability / performance20%92
Tool hygiene15%74
Transparency / provenance15%70

Observed behavior

No proxied traffic observed for this host yet. Connect it at /connect and its grade gains a measured Reliability score + per-tool behavioral evidence — the half a static scan can't produce.

Findings

No blocking issues found in the static + spec checks.
Grade another server

We re-grade goldenmatch-mcp-production.up.railway.app on a schedule and alert your Slack/webhook the moment its tools change or its grade drops — rug-pull insurance for the connection.

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A 1200×630 card with the grade + audit — drop it in a post, Slack, or your repo.

MCP Trust report card — goldenmatch-mcp-production.up.railway.app grade B
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A live badge — it re-verifies itself and shows current stability. Static scorecards can't. Paste it in your README or site to show users you're independently audited.

MCP Trust Grade B · wmcp.sh
[![MCP Trust Grade B](https://wmcp.sh/mcp/grade/goldenmatch-mcp-production.up.railway.app/badge.svg)](https://wmcp.sh/mcp/grade/goldenmatch-mcp-production.up.railway.app)
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Agents: check this before connecting

Add the wmcp.sh trust oracle as an MCP server and call grade_mcp_server / check_mcp_drift in your agent's pre-connection gate:

https://wmcp.sh/mcp/trust
How this grade is computed. An open, independent rubric — Spec conformance (20%), Security mapped to the OWASP MCP Top 10 (30%), Reliability (20%), Tool hygiene (15%), Transparency (15%) — run by connecting to the server and inspecting its real MCP surface. The grade is free and identical whether or not the operator pays. v1 uses static + spec signals from a single connection; continuous uptime, real latency, and annotation-truthing (declared readOnly vs observed behavior) layer on via the wmcp.sh proxy.