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  1. Home
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  3. Coinversa Pulse — MCP Server
  4. vs Tradememory Protocol
Side-by-Side Model Context Protocol Comparison

Coinversa Pulse — MCP Server vs Tradememory Protocol

In-depth architectural comparison of the Coinversa Pulse — MCP Server 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

Coinversa Pulse — MCP Server
Finance & Fintech · Local stdio
Quality: 55/100 (Good) | Auth: No auth required
Tradememory Protocol
Finance & Fintech · Local stdio
Quality: 68/100 (Great) | Auth: No auth required
Verdict Summary: Choose Coinversa Pulse — MCP Server 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?

Coinversa Pulse — MCP Server logo

Choose Coinversa Pulse — MCP Server 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: pulse_global_stats, list_markets, pulse_market_overview.
Explore Coinversa Pulse — MCP Server 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
Coinversa Pulse — MCP Server logo
Coinversa Pulse — MCP Server
Finance & Fintech
Tradememory Protocol logo
Tradememory Protocol
mnemox-ai
Finance & Fintech
SummaryCrypto intelligence for AI agents. Hyperliquid trader analytics, cohorts, and live market data.Glama 🐍 🏠 - Structured 3-layer memory system (trades → patterns → strategy) for AI trading agents. Supports MT5, Binance, and Alpaca.
Category & ScopeFinance & Fintech

Tools & Capabilities Breakdown

Coinversa Pulse — MCP Server Tools (70)

pulse_global_stats
Total traders, trades, volume, PnL across Hyperliquid — call this for current coverage numbers
list_markets
Canonical market discovery — every symbol with dex, price, volume, funding rate, OI
pulse_market_overview
Deprecated alias for `list_markets` (same payload)
pulse_leaderboard
Top traders ranked by PnL, win rate, volume, score, or risk-adjusted returns
pulse_hidden_gems
Underrated high-performers most platforms miss
pulse_most_traded_coins

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).

Coinversa Pulse — MCP Server Configuration
mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "mcp-server-63": {
      "command": "npx",
      "args": [
        "-y",
        "@coinversaa/mcp-server@0.12.0"
      ]
    }
  }
}
Tradememory Protocol Configuration
mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "mnemox-ai-tradememory-protocol": {
      "command": "uvx",
      "args": [
        "tradememory-protocol"
      ]
    }
  }
}

Frequently Asked Questions

Coinversa Pulse — MCP Server is categorized under Finance & Fintech and uses a local stdio subprocess. In contrast, Tradememory Protocol belongs to Finance & Fintech using local stdio subprocess. Select Coinversa Pulse — MCP Server when you need capabilities focused on finance & fintech and Tradememory Protocol when you require tools for finance & fintech.

More alternatives to Coinversa Pulse — MCP ServerMore alternatives to Tradememory ProtocolFinance & Fintech category hub

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Popular comparisons with Tradememory Protocol

Finance & Fintech
Quality signal55/100 (Good)68/100 (Great)
Transport ProtocolLocal Subprocess (stdio)Local 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 signalnpx · highuvx · high
Engagement & Health 0 views 0 copies 0 upvotes 7 stars 4 views 0 copies 0 upvotes 1,420 stars
Verified / OfficialCommunity ListingCommunity Listing
Open full listingView Coinversa Pulse — MCP Server ListingView Tradememory Protocol Listing
Most actively traded coins ranked by volume and trade count
pulse_biggest_trades
Biggest winning or losing trades across all of Hyperliquid
pulse_recent_trades
Biggest trades in the last N minutes/hours
pulse_token_leaderboard
Top traders for a specific coin
list_assets
Directory of canonical assets — every asset grouped by economic exposure, with venues, synonyms, and a cross-market flag. Use `crossMarketOnly: true` to filter to multi-venue assets.
list_asset
Single canonical lookup with venue breakdown. Accepts synonyms — `list_asset({canonical: "PAXG"})` returns the GOLD asset.
pulse_cross_market_asset
Aggregated per-venue long/short/bias/OI for one asset, plus cross-venue totals and a `biasRange` metric (venues agree vs disagree on direction). The agent-native answer to "is X crowded?" and "do venues disagree on Y?".
+58 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
MCP Server Kalshi logo
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