Varrd vs Tradememory Protocol — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Varrd vs Tradememory Protocol
In-depth architectural comparison of the Varrd and Tradememory Protocol 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
Varrd
Finance & Fintech · Local stdio
Quality: 63/100 (Good) | Auth: No auth required
Tradememory Protocol
Finance & Fintech · Local stdio
Quality: 68/100 (Great) | Auth: No auth required
Verdict Summary: Choose Varrd if you need specialized Finance & Fintech tools running via a local process. Choose Tradememory Protocol 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?
Choose Varrd 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).
THE PRIMARY TOOL — start here. FREE at depth=0, always safe to call.
Live feed of THIS USER'S OWN statistically validated trading edges — the ones on their account — running 24/7 against real market data. See which of YOUR edges are firing right now, get trade levels, or audit the full methodology. Scoped to the connected account: if the user has no edges yet, this returns none (it is NOT a general/shared library).
THREE TIERS:
depth=0 (FREE — call this first): See which of YOUR edges are firing right now, pending bar close, or actively in trades. Markets and status only — no direction, no stats. Get a sense of what's live.
depth=1 ($0.50): Unlock direction, occurrence count, EV/trade, stop-loss, take-profit, hold horizon, and current entry prices for ALL active edges in one request.
depth=2 ($1 per edge, $5 for all): Full methodology — the actual formula, setup code, how the edge was discovered, edge decay analysis, complete performance analytics (Sharpe, drawdown, equity curve, profit factor). Machine-readable so any AI can audit the statistical rigor. Includes drill-down sections (free after purchase): setup_code, horizons, analytics, occurrences, and view (interactive chart link for your user, 15 min).
Every edge in this library is Bonferroni-corrected, tested against both zero returns and market baseline, with K-tracking to prevent p-hacking. Out-of-sample validated. Full transparency.
varrd_ai
Talk to VARRD AI (~$0.25/turn). Describe any trading idea in plain language and the system handles everything — loading decades of market data, charting your pattern, running statistical tests, backtesting with stops, and generating exact trade setups.
MULTI-TURN: First call creates a session. Keep calling with the same session_id, following context.next_actions each time.
1. Your idea -> VARRD charts pattern
2. 'test it' -> statistical test (event study or backtest)
3. 'show me the trade setup' -> exact entry/stop/target prices
HYPOTHESIS INTEGRITY (critical):
VARRD tests ONE hypothesis at a time — one formula, one setup. Never combine multiple setups into one formula or ask to 'test all' — each idea must be tested as a separate hypothesis for the statistics to be valid. Say 'start a new hypothesis' between ideas to reset cleanly.
- ALLOWED: Test the SAME setup across multiple markets ('test this on ES, NQ, and CL') — same formula, different data.
- NOT ALLOWED: Test multiple DIFFERENT formulas/setups at once — each is a separate hypothesis requiring its own chart-test-result cycle.
If ELROND council returns 4 setups, test each one separately: chart setup 1 -> test -> results -> 'start new hypothesis' -> chart setup 2 -> etc.
KEY CAPABILITIES you can ask for:
- 'Use the ELROND council on [market]' -> 8 expert investigators
- 'Optimize the stop loss and take profit' -> SL/TP grid search
- 'Test this on ES, NQ, and CL' -> multi-market testing
- 'Simulate trading this with 1.5 ATR stop' -> backtest with stops
EDGE VERDICTS in context.edge_verdict after testing:
- STRONG EDGE: Significant vs zero AND vs market baseline
- MARGINAL: Significant vs zero only (beats nothing, but real signal)
- PINNED: Significant vs market only (flat returns but different from market)
- NO EDGE: Neither significant test passed
TERMINAL STATES: Stop when context.has_edge is true (edge found) or false (no edge — valid result). Always read context.next_actions.
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).
Varrd is categorized under Finance & Fintech and uses a local stdio subprocess. In contrast, Tradememory Protocol belongs to Finance & Fintech using local stdio subprocess. Select Varrd when you need capabilities focused on finance & fintech and Tradememory Protocol when you require tools for finance & fintech.
Search your saved hypotheses by keyword or natural language query. Returns matching strategies ranked by relevance, with key stats (win rate, Sharpe, edge status). Use this to find strategies you've already validated.
get_hypothesis
Get full detail for a specific hypothesis/strategy. Returns formula, entry/exit rules, direction, performance metrics (win rate, Sharpe, profit factor, max drawdown), version history, and trade levels. Everything an agent needs to understand and act on a strategy.
check_balance
Check your credit balance and see available credit packs. Free — no credits consumed. Also auto-detects completed payments — call this after your user pays via a checkout link to confirm credits were added. If payment went through, the response includes recovered_cents.
buy_credits
Buy credits for the edge library and AI research. Default $5 minimum. Free — no credits consumed to call this.
TWO PAYMENT METHODS:
card (default): Returns a Stripe Checkout link for your user to click and pay. After payment, call check_balance to confirm credits were added.
crypto: USDC on Base. Fully autonomous — no human needed. Three steps:
1. buy_credits(payment_method='crypto') → returns deposit address + payment_intent_id
2. Send USDC to the deposit address (use your wallet tool)
3. buy_credits(payment_intent_id='pi_...') → confirms payment, credits added instantly
If you have wallet access, this is the fastest path — fully machine-to-machine.
reset_session
Kill a broken research session and start fresh. Use this when a session gets stuck, produces errors, or enters a bad state. Free — no credits consumed. After resetting, call research without a session_id to start a new clean session.
autonomous_varrd_ai
Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked.
BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of.
Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea.
Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.
get_briefed
Get a personalized market news briefing based on your validated edge library. Profiles your strategies, searches today's news for the instruments and setups you actually trade, and writes a concise digest connecting each headline to your specific book.
Each news item includes a ↳ line tying it to your actual positions and edges (e.g. 'your ES momentum setups', 'your GC mean-reversion edge').
Requires at least 5 strong edges in your library. Costs credits.
Tradememory Protocol Tools (20)
get_strategy_performance
Get aggregate performance stats per strategy.
Use this to evaluate which strategies are working and which need adjustment.
get_trade_reflection
Get the full context and reflection for a specific trade.
Use this to deep-dive into a particular trade's reasoning and lessons.
remember_trade
Store 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_memories
Recall 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_analysis
Get behavioral analysis from procedural memory.
Returns aggregate trading behavior stats: hold times, disposition ratio,
lot sizing variance, and Kelly criterion comparison.
get_agent_state
Get 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_plan
Create 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_plans
Check 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_data
Fetch 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_patterns
Discover 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_backtest
Backtest 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_strategy
Run 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.