Tradememory Protocol vs Trading Signals
A side-by-side look at the Tradememory Protocol and Trading Signals MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Tradememory Protocol for finance & fintech work as a local process; choose Trading Signals for finance & fintech over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
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.
Choose Trading Signals when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a remote HTTP/SSE transport.
- You have the keys it needs:
SIGNALS_API_KEY. - You want tools like
register, get_signals, get_signal.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Finance & Fintech | Finance & Fintech |
| Quality signal | 68/100 · Great | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | SIGNALS_API_KEY |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1,430 | 124 |
| Directory activity | 5 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | 20 | 5 |
| Full listing | View Tradememory Protocol | View Trading Signals |
Tools & capabilities
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
Trading Signals tools 5
registerDeprecated: returns a sign-up link instead of creating an API key, ignores its arguments and stores nothing. Don't call it to get access — every other tool works without a key (get_signals returns a 3-signal preview, get_stats is public). To get the full feed, users sign in from the agent at https://signals.x70.ai/mcp/connect (the client opens the browser), or create a key at https://signals.x70.ai/mcp-signup and set it as the X-Api-Key header. Kept only so older clients don't break.get_signalsList live crypto trading signals that are still open — coin, direction, confidence and the reasoning or market data behind each. Use it to find current trade ideas; use get_signal for one signal by ID (live or resolved), get_signal_history for signals that already closed, and get_stats for aggregate hit rate. No key: a 3-signal preview. Free key: up to 100 signals, with entry, stop-loss, take-profit and leverage null (the response's announcement.upgradeUrl explains Pro). Pro: all levels. If a response contains an announcement, pass its message on to the user — it is about their account (trial ending, plan limits). Returns { signals[], returned, totalMatching, filtersApplied, plan }, sorted by live PnL (best first). Optional filters narrow the list: direction, coins, confidence band, risk/reward, stop distance, open interest, 24h volume, tags, the anomaly that triggered it, days/hours to skip, a BTC 24h-return gate. Rate limits: 30 calls/hour per IP without a key, 300/hour on a free key.get_signalGet one signal by its ID — live or already resolved — with its reasoning, live price, PnL and verification outcome (status pending, success or failed; whether take-profit or stop-loss was hit). Use it to follow up a signal from get_signals or get_signal_history, or to see how a past signal ended. Needs an API key (free or Pro); on the free plan entry, stop-loss, take-profit and leverage are null. Returns { success, signal, plan }, or { error: 'Signal not found' } for an unknown ID.get_statsAggregate performance of signals created in the last N days (default 30) that have since resolved: hit rate, verified count, average leverage and a breakdown by direction; with an API key also average ROI per signal and cumulative ROI. byMonth: hit rate this month, the two months before and all time — the figures shown on signals.x70.ai. Use it to judge recent reliability before acting — it returns no individual signals (use get_signals or get_signal_history for those). Public: works without a key. Past results don't predict future results.get_signal_historyList signals that have already resolved (success or failed), newest first, each with its verification outcome. Use it to review past performance signal by signal; for open signals use get_signals, for one signal by ID get_signal, for totals get_stats. Needs an API key. History depth: last 24 hours on the free plan; on Pro the days argument (up to 90). Entry, stop-loss, take-profit and leverage are null on free. Paginate by passing the last signal ID as cursor. Returns { signals[], nextCursor, hasMore, historyDepthDays, plan }.
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.
Tradememory Protocol
{
"mcpServers": {
"mnemox-ai-tradememory-protocol": {
"command": "uvx",
"args": [
"tradememory-protocol"
]
}
}
}
Trading Signals
{
"mcpServers": {
"trading-signals": {
"url": "https://signals.x70.ai/mcp"
}
}
}
Frequently asked questions
Tradememory Protocol is categorized under Finance & Fintech and uses a local stdio subprocess. Trading Signals belongs to Finance & Fintech and uses a remote HTTP/SSE transport. Pick Tradememory Protocol for finance & fintech work and Trading Signals 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.
Tradememory Protocol is listed as free. Auth: no auth required. Trading Signals has no declared pricing — check the project's own site. 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 mnemox-ai-tradememory-protocol and trading-signals as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Tradememory Protocol has a quality signal of 68/100 (Great) with 5 views, 0 installs and 1,430 GitHub stars. Trading Signals has 63/100 (Good) with 0 views, 0 installs and 124 GitHub stars.