Trading Signal & Mark… vs Web3 Signals MCP | AllMCPs
Side-by-Side Model Context Protocol Comparison
Trading Signal & Market Regime MCP Server vs Web3 Signals MCP
In-depth architectural comparison of the Trading Signal & Market Regime MCP Server and Web3 Signals MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Trading Signal & Market Regime MCP Server
Finance & Fintech · Local stdio
Quality: 52/100 (Good) | Auth: No auth required
Web3 Signals MCP
Finance & Fintech · Local stdio
Quality: 31/100 (Emerging) | Auth: No auth required
Verdict Summary: Choose Trading Signal & Market Regime MCP Server if you need specialized Finance & Fintech tools running via a local process. Choose Web3 Signals MCP if your workspace requires Finance & Fintech integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
T
Choose Trading Signal & Market Regime MCP Server when:
You need dedicated capabilities in the Finance & Fintech domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Trading Signal & Market Regime MCP Server Tools (37)
get_daily_brief
Single-call market overview: macro regime + top 5 strong signals + yesterday's paper-trading P&L + active prediction count + Korean narrative. The natural first call for "what's the market doing today?"
get_ledger_integrity
Tamper-evidence for the prediction ledger: a daily SHA-256 hash chain over all created/resolved prediction rows, with the exact canonical recipe published so any third party can recompute and verify. The strongest trust primitive OneQAZ offers — judgments are chained *before* outcomes are known.
get_resolved_predictions
Raw row-level prediction ledger: every macro regime prediction's full lifecycle (`created_at` → `resolved_at` → outcome) — audit the evidence judgment by judgment.
get_trade_outcomes_bulk
Cursor-paginated bulk export of the prediction → trade → outcome chain (paper trades with realized P&L, linked to the preceding signal prediction) — compute your own hit rates instead of trusting ours.
get_performance_metrics
Portfolio-level MDD / Sharpe / Sortino / Calmar / win-rate per market and account type (`paper` / `live`), optional daily equity curve.
get_signal_calibration
Reliability diagram data for signal confidence: realized hit rate per confidence bucket with ECE summary — verify whether a 0.9-confidence signal actually hits ~90%.
search
ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong signals across all three markets. Result ids are consumable by `fetch`.
fetch
Connector-standard fetch of a single result by id returned from `search`.
get_prediction_accuracy
Verified hit rates per macro category (with `sample_count`)
get_backtest_tuning_state
Active tuning parameters + last recalibration timestamp
get_monthly_accuracy_trend
Rolling 12-month accuracy per category
get_news_leading_indicator_performance
Pre-news detection lead time + accuracy
+25 more tools listed on main page
Web3 Signals MCP Tools (0)
No explicit tool names declared in metadata yet. Check project README on main listing page.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Trading Signal & Market Regime MCP Server Configuration
Trading Signal & Market Regime MCP Server is categorized under Finance & Fintech and uses a local stdio subprocess. In contrast, Web3 Signals MCP belongs to Finance & Fintech using local stdio subprocess. Select Trading Signal & Market Regime MCP Server when you need capabilities focused on finance & fintech and Web3 Signals MCP when you require tools for finance & fintech.