In-depth architectural comparison of the Tradememory Protocol and Zensei 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
Tradememory Protocol
Finance & Fintech · Local stdio
Quality: 68/100 (Great) | Auth: No auth required
Zensei MCP
Finance & Fintech · Remote HTTP/SSE
Quality: 74/100 (Great) | Auth: OAuth 2.0
Verdict Summary: Choose Tradememory Protocol if you need specialized Finance & Fintech tools running via a local process. Choose Zensei MCP if your workspace requires Finance & Fintech integration with remote web transport. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Tradememory Protocol 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).
Glama 🐍 🏠 - Structured 3-layer memory system (trades → patterns → strategy) for AI trading agents. Supports MT5, Binance, and Alpaca.
Live sector rotation data for AI agents. Tools: sector rotation status (100+ sectors classified as leading, lagging, improving or weakening vs the S&P 500), sector-to-stock drill-down (the stocks driving each move), and macro regime (Zensei Index risk-on / risk-off read). Remote server, OAuth with a Zensei account.
Tools & Capabilities Breakdown
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
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).
Tradememory Protocol is categorized under Finance & Fintech and uses a local stdio subprocess. In contrast, Zensei MCP belongs to Finance & Fintech using remote streaming HTTP/SSE transport. Select Tradememory Protocol when you need capabilities focused on finance & fintech and Zensei MCP when you require tools for finance & fintech.
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.
+8 more tools listed on main page
Zensei MCP Tools (4)
zensei_index_about
What the Zensei Index is, how to read the score, how it is built, and what this server serves. Same wording as the website FAQ. Call it once before interpreting any score.
zensei_index_today
Today's Zensei Index for U.S. equities: the final score, its regime label, and the three layer scores (structural conditions, tactical filters, regime confirmations). The Zensei Index is a RISK score from 0 to 100: LOWER IS HEALTHIER. 0-20 Supportive, 20-40 Vulnerable, 40-70 Stressed, 70-100 Restrictive. A rising score means conditions are deteriorating, not improving. Call zensei_index_indicators to see why the score sits where it does.
zensei_index_indicators
The ten indicators behind today's Zensei Index, each with its current status, the layer it scores into, its weight in that layer, and the same explanation the website shows. Use it to explain why the index sits where it does. Indicator statuses run from healthiest to most stressed: Clear or Inactive (no stress), Watch (early warning), Stress (confirmed stress), Activated or Danger (full signal). Calibrating means not enough data yet; Unknown means no data.
zensei_index_summary
The latest written summary of the Zensei Index, with the score and regime it was written for. A summary is rewritten only when the indicators change. Check `stale` and `ageDays` before quoting it as today's view, and prefer zensei_index_today for the current number. The Zensei Index is a RISK score from 0 to 100: LOWER IS HEALTHIER. 0-20 Supportive, 20-40 Vulnerable, 40-70 Stressed, 70-100 Restrictive. A rising score means conditions are deteriorating, not improving.