Tradememory Protocol vs Quant Research MCP
A side-by-side look at the Tradememory Protocol and Quant Research MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Tradememory Protocol for finance & fintech work as a local process; choose Quant Research MCP for finance & fintech as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Tradememory Protocol when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
get_strategy_performance, get_trade_reflection, remember_trade.
Choose Quant Research MCP when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a local stdio subprocess transport.
- You want tools like
quant_list_methods, quant_describe_method, quant_screen_universe.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Finance & Fintech | Finance & Fintech |
| Quality signal | 68/100 · Great | 48/100 · Fair |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1,425 | 0 |
| Directory activity | 4 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 20 | 13 |
| Full listing | View Tradememory Protocol | View Quant Research MCP |
Tools & capabilities
Tradememory Protocol tools 20
get_strategy_performanceGet aggregate performance stats per strategy. Use this to evaluate which strategies are working and which need adjustment.get_trade_reflectionGet the full context and reflection for a specific trade. Use this to deep-dive into a particular trade's reasoning and lessons.remember_tradeStore a trade into OWM multi-layer memory with automatic updates. Writes to episodic memory and automatically updates semantic (Bayesian), procedural (running averages + hold time + Kelly), and affective (EWMA confidence/streaks). Also writes to trade_records for backward compatibility.recall_memoriesRecall memories using OWM outcome-weighted scoring. Queries episodic and semantic memories, scores them by outcome quality, context similarity, recency, confidence, and affective modulation. Returns ranked memories with score breakdown.get_behavioral_analysisGet behavioral analysis from procedural memory. Returns aggregate trading behavior stats: hold times, disposition ratio, lot sizing variance, and Kelly criterion comparison.get_agent_stateGet the current agent affective state (confidence, risk, drawdown). Returns confidence level, risk appetite, drawdown percentage, win/loss streaks, equity tracking, and a recommended action based on current drawdown severity.create_trading_planCreate a prospective trading plan that activates when conditions are met. Stores a rule-based plan in prospective memory. The plan stays active until triggered, expired, or manually cancelled.check_active_plansCheck active trading plans against current market context. Queries all active prospective plans, expires any past their expiry date, and matches remaining plans against the provided context.evolution_fetch_market_dataFetch OHLCV market data from Binance for evolution analysis. Downloads historical price bars for backtesting and pattern discovery. Use this before discover_patterns or run_backtest to get data.evolution_discover_patternsDiscover trading patterns from market data using LLM analysis. Uses Claude to analyze OHLCV data and generate candidate trading patterns with entry/exit conditions. Each pattern can be backtested afterward.evolution_run_backtestBacktest a candidate pattern against historical OHLCV data. Takes a pattern dict (from discover_patterns) and runs a vectorized backtest. Returns fitness metrics: Sharpe ratio, win rate, trade count, max drawdown, total PnL.evolution_evolve_strategyRun full evolution loop — generate, backtest, select, eliminate. Multi-generation strategy evolution: generates candidate patterns via LLM, backtests on in-sample data, validates survivors on out-of-sample data, eliminates weak hypotheses. Returns graduated strategies and graveyard.- +8 more on the listing page
Quant Research MCP tools 13
quant_list_methodsList registered methods (key, paper, timeframe, regimes).quant_describe_methodFull detail + citation for one method.quant_screen_universeRank tickers by gap / rel-volume / ATR%.quant_compute_indicatorsEMA9/20, RSI, ATR, VWAP, Hurst for a ticker.quant_detect_regimeHurst → TRENDING / MEAN_REVERTING / RANDOM_WALK.quant_analyze_setupRun a per-ticker method → signal + entry/stop/target (+ HTF context).quant_analyze_universeRun a universe method: momentum ranking, pairs spread.quant_backtest_methodValidate a method**: replay its own analyze() over history → win rate, expectancy, drawdown, IS/OOS halves.quant_check_eventsNext earnings (+days), ex-dividend — binary-event risk.quant_portfolio_riskStateless heat / correlation / concentration check + candidate verdict.quant_market_contextSPY/QQQ/IWM, VIX, 11 sectors ranked, risk-on/off breadth.quant_score_decisionDecision helper**: setup (+ sentiment, earnings, heat) → score, verdict, size, timeframe-aware execution plan. Deterministic.- +1 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Tradememory Protocol
{
"mcpServers": {
"mnemox-ai-tradememory-protocol": {
"command": "uvx",
"args": [
"tradememory-protocol"
]
}
}
}
Quant Research MCP
{
"mcpServers": {
"quant-research-mcp": {
"command": "uvx",
"args": [
"quant-research-mcp"
]
}
}
}
Frequently asked questions
Tradememory Protocol is categorized under Finance & Fintech and uses a local stdio subprocess. Quant Research MCP belongs to Finance & Fintech and uses a local stdio subprocess. Pick Tradememory Protocol for finance & fintech work and Quant Research MCP for finance & fintech.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Tradememory Protocol is listed as free. Auth: no auth required. Quant Research MCP has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add mnemox-ai-tradememory-protocol and quant-research-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Tradememory Protocol has a quality signal of 68/100 (Great) with 4 views, 0 installs and 1,425 GitHub stars. Quant Research MCP has 48/100 (Fair) with 2 views, 0 installs and 0 GitHub stars.