In-depth architectural comparison of the Obra Cfo 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
Obra Cfo
Finance & Fintech · Local stdio
Quality: 60/100 (Good) | Auth: No auth required
Tradememory Protocol
Finance & Fintech · Local stdio
Quality: 68/100 (Great) | Auth: No auth required
Verdict Summary: Choose Obra Cfo 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 Obra Cfo 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).
Return the framework for a fundable pitch deck at your stage: the slide structure with an evidence bar for each, the research to run first (as paste-in prompts for your Claude), and the artifacts to produce before the deck exists (business plan, pricing, go-to-market). Use this when you have no deck yet or a weak one. It reads your Obra CTO report if present. Then build the deck and ask the CFO to rank it.
prepare_assessment
Read the founder's funding materials (deck, plan, financials, one-pager) from a local path and return their contents with a rigorous assessment rubric. YOU, the host model, then assess them (separate facts from assumptions, extract the thesis, rate each dimension with evidence and confidence, find the weakest assumption, run the six-persona committee) and call score_funding_readiness with the structured result. Reads the Obra CTO report if present so technical claims rest on verified evidence. Materials stay local.
score_funding_readiness
Turn your structured assessment (from prepare_assessment) into a stage-calibrated funding-readiness dashboard: investment score, confidence, risk register, the facts/assumptions/opinions ledger, your weakest assumption, what must be true, the unknowns, and the investment committee. Reads the Obra CTO report for technical risk.
Tradememory Protocol Tools (20)
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).
Obra Cfo is categorized under Finance & Fintech and uses a local stdio subprocess. In contrast, Tradememory Protocol belongs to Finance & Fintech using local stdio subprocess. Select Obra Cfo when you need capabilities focused on finance & fintech and Tradememory Protocol when you require tools for finance & fintech.
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.