Papertrade AI vs Tradememory Protocol
A side-by-side look at the Papertrade AI and Tradememory Protocol MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Papertrade AI for finance & fintech work as a local process; choose Tradememory Protocol 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 Papertrade AI when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: OAuth 2.0.
- You want tools like
get_markets, get_price_chart, get_protocol_stats.
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.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Finance & Fintech | Finance & Fintech |
| Quality signal | 56/100 · Good | 68/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | OAuth 2.0 | No auth required |
| Pricing | Free | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 1,431 |
| Directory activity | 0 views 0 installs 0 upvotes | 5 views 0 installs 0 upvotes |
| Tools | 18 | 20 |
| Full listing | View Papertrade AI | View Tradememory Protocol |
Tools & capabilities
Papertrade AI tools 18
get_marketsLive Papertrade perp markets (BTC, ETH): Hyperliquid mark/bid/ask, 24h change, Papertrade 24h volume, open interest vs caps, max leverage, minimum trade size and whether each market is open.get_price_chartOHLCV candles for BTC or ETH from Hyperliquid (the price source Papertrade settles against), with range stats and a TradingView link.get_protocol_statsPapertrade protocol analytics: TVL, margin, LP, lifetime volume, open positions, fees, PAPER supply and staking, plus daily history (volume, trader PnL, liquidations, users).get_liquidation_mapWhere open Papertrade positions get liquidated: notional and position count of longs and shorts by price bucket around the current price, with the largest clusters.get_leaderboardPapertrade trader leaderboard for a time window, sortable by PnL, volume, balance and more.get_top_tradesThe most profitable individual Papertrade positions in a window: who, market, side, leverage, entry, exit and PnL.get_recent_tradesThe latest opens, closes and liquidations across all of Papertrade, optionally filtered by market or wallet.get_accountA Papertrade account: free balance, locked margin, equity, every open position with live estimated PnL, bust price and distance to liquidation, pending intents, session keys and lifetime stats.get_trade_historyClosed and liquidated positions for a wallet, newest first, with entry, exit, settled PnL, fees and PAPER minted.get_portfolio_historyEquity curve for a wallet over 1h, 1d, 1w or all time.calculate_positionPure math for a hypothetical position: bust price, liquidation distance and settled PnL at a target exit, using the exchange formula (win curve, deadband, 2% win fee).get_deposit_infoHow to fund a Papertrade account: the wallet's personal deposit proxy address (USDC on HyperCore spot), minimums, the activation fee, and the wallet's current HyperEVM USDC/HYPE balances.- +6 more on the listing page
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
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.
Papertrade AI
{
"mcpServers": {
"papertrade-ai": {
"command": "npx",
"args": [
"-y",
"papertrade-ai"
]
}
}
}
Tradememory Protocol
{
"mcpServers": {
"mnemox-ai-tradememory-protocol": {
"command": "uvx",
"args": [
"tradememory-protocol"
]
}
}
}
Frequently asked questions
Papertrade AI is categorized under Finance & Fintech and uses a local stdio subprocess. Tradememory Protocol belongs to Finance & Fintech and uses a local stdio subprocess. Pick Papertrade AI for finance & fintech work and Tradememory Protocol 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.
Papertrade AI is listed as free. Auth: oauth 2.0. Tradememory Protocol is listed as free. Auth: no auth required. 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 papertrade-ai and mnemox-ai-tradememory-protocol as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Papertrade AI has a quality signal of 56/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Tradememory Protocol has 68/100 (Great) with 5 views, 0 installs and 1,431 GitHub stars.