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  1. Home
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  3. Madeonsol
  4. vs Tradememory Protocol
Side-by-Side Model Context Protocol Comparison

Madeonsol vs Tradememory Protocol

In-depth architectural comparison of the Madeonsol 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

Madeonsol
Finance & Fintech · Remote HTTP/SSE
Quality: 57/100 (Good) | Auth: No auth required
Tradememory Protocol
Finance & Fintech · Local stdio
Quality: 68/100 (Great) | Auth: No auth required
Verdict Summary: Choose Madeonsol if you need specialized Finance & Fintech tools running via a hosted cloud SSE transport. 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?

Madeonsol logo

Choose Madeonsol when:

  • You need dedicated capabilities in the Finance & Fintech domain.
  • You prefer remote streaming HTTP/SSE transport architecture.
  • Your security boundary fits: No auth required (Free / Open Source).
  • Primary tools included: madeonsol_kol_feed, madeonsol_kol_coordination, madeonsol_kol_first_touches.
Explore Madeonsol Details
Tradememory Protocol logo

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).
  • Primary tools included: get_strategy_performance, get_trade_reflection, remember_trade.
Explore Tradememory Protocol Details

Feature & Specification Comparison

Specification
Madeonsol logo
Madeonsol
Finance & Fintech
Tradememory Protocol logo
Tradememory Protocol
mnemox-ai
Finance & Fintech
SummarySolana KOL trading intelligence & deployer analytics via x402 micropayments or API key.Glama 🐍 🏠 - Structured 3-layer memory system (trades → patterns → strategy) for AI trading agents. Supports MT5, Binance, and Alpaca.
Category & ScopeFinance & Fintech

Tools & Capabilities Breakdown

Madeonsol Tools (80)

madeonsol_kol_feed
Real-time KOL trade feed (1,000+ wallets)
madeonsol_kol_coordination
Multi-KOL convergence signals (v1.1) — peak-density window, exit detection, 0-100 score
madeonsol_kol_first_touches
First-KOL-touch events — backtested scout signal. Filter by scout tier, winrate, token age, mint suffix
madeonsol_kol_leaderboard
KOL PnL and win rate rankings (180 days of history; periods: today, 7d, 30d, 90d, 180d)
madeonsol_kol_pairs
KOL affinity matrix — which KOLs co-trade the same tokens
madeonsol_kol_hot_tokens

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).

Madeonsol Configuration
mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "madeonsol": {
      "url": "http://127.0.0.1:3100/mcp"
    }
  }
}
Tradememory Protocol Configuration
mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "mnemox-ai-tradememory-protocol": {
      "command": "uvx",
      "args": [
        "tradememory-protocol"
      ]
    }
  }
}

Frequently Asked Questions

Madeonsol is categorized under Finance & Fintech and uses a remote streaming HTTP/SSE transport. In contrast, Tradememory Protocol belongs to Finance & Fintech using local stdio subprocess. Select Madeonsol when you need capabilities focused on finance & fintech and Tradememory Protocol when you require tools for finance & fintech.

More alternatives to MadeonsolMore alternatives to Tradememory ProtocolFinance & Fintech category hub

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Popular comparisons with Tradememory Protocol

Finance & Fintech
Quality signal57/100 (Good)68/100 (Great)
Transport ProtocolRemote HTTP/SSELocal Subprocess (stdio)
Auth RequirementNo auth requiredNo auth required
Pricing ModelFree / Open SourceFree / Open Source
Required Env VarsNone requiredNone required
Compatible Clients
Claude DesktopCursorWindsurfClineVS Code
Claude DesktopCursorWindsurfClineVS Code
Install path signalRemote (HTTP/SSE) · highuvx · high
Engagement & Health 0 views 0 copies 0 upvotes 2 stars 4 views 0 copies 0 upvotes 1,420 stars
Verified / OfficialCommunity ListingCommunity Listing
Open full listingView Madeonsol ListingView Tradememory Protocol Listing
KOL momentum tokens — accelerating buy interest
madeonsol_kol_trending_tokens
Tokens ranked by KOL buy volume (5m–12h windows). ULTRA adds full KOL wallet addresses.
madeonsol_kol_pnl
Deep per-wallet PnL: equity curve, risk metrics, closed positions. ULTRA adds open positions (tokens bought but not yet sold).
madeonsol_kol_timing
KOL entry/exit timing profile — available on all tiers
madeonsol_deployer_alerts
Pump.fun deployer launches with KOL enrichment. Filter by tier (elite/good/moderate/rising/cold). ULTRA unlocks full pagination. Each alert's `deployers` now includes `deployer_sol_balance` — the deployer wallet's SOL balance at alert time (null for historical rows).
madeonsol_deployer_trajectory
Deployer skill curve — streaks, rolling bond rate, trend — available on all tiers
madeonsol_deployer_history
A pump.fun deployer's daily reputation time-series (`bonding_rate`, `recent_bond_rate`, `tier`, `avg_peak_mc` per day) — backtest deployer signals at launch time without look-ahead bias. `limit` 1–365 (default 90)
+68 more tools listed on main page

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.
+8 more tools listed on main page
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