Finance Toolkit vs Tradememory Protocol
A side-by-side look at the Finance Toolkit 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 Finance Toolkit 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 Finance Toolkit when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a local stdio subprocess transport.
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 | 49/100 · Fair | 68/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | No auth required |
| Pricing | Free | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 5,446 | 1,431 |
| Directory activity | 4 views 0 installs 0 upvotes | 5 views 0 installs 0 upvotes |
| Tools | Not listed | 20 |
| Full listing | View Finance Toolkit | View Tradememory Protocol |
Tools & capabilities
Finance Toolkit tools
No tool names declared yet. Check the project README from 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.
Finance Toolkit
{
"mcpServers": {
"finance-toolkit": {
"command": "uvx",
"args": [
"financetoolkit"
]
}
}
}
Tradememory Protocol
{
"mcpServers": {
"mnemox-ai-tradememory-protocol": {
"command": "uvx",
"args": [
"tradememory-protocol"
]
}
}
}
Frequently asked questions
Finance Toolkit is categorized under Finance & Fintech and uses a local stdio subprocess. Tradememory Protocol belongs to Finance & Fintech and uses a local stdio subprocess. Pick Finance Toolkit 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.
Finance Toolkit is listed as free. 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 finance-toolkit and mnemox-ai-tradememory-protocol as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Finance Toolkit has a quality signal of 49/100 (Fair) with 4 views, 0 installs and 5,446 GitHub stars. Tradememory Protocol has 68/100 (Great) with 5 views, 0 installs and 1,431 GitHub stars.