Tradememory Protocol vs Graph Polymarket MCP
A side-by-side look at the Tradememory Protocol and Graph Polymarket MCP 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 Graph Polymarket MCP 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 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 Graph Polymarket MCP when
- You need dedicated capabilities in the Finance & Fintech domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
GRAPH_API_KEY, MCP_HTTP_PORT. - You want tools like
list_subgraphs, get_subgraph_schema, query_subgraph.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Finance & Fintech | Finance & Fintech |
| Quality signal | 68/100 · Great | 61/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | API key required |
| Pricing | Free | Free |
| Required env vars | None listed | GRAPH_API_KEYMCP_HTTP_PORT |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1,425 | 4 |
| Directory activity | 4 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 20 | 35 |
| Full listing | View Tradememory Protocol | View Graph Polymarket MCP |
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
Graph Polymarket MCP tools 35
list_subgraphsList all available Polymarket subgraphs with descriptions and key entitiesget_subgraph_schemaGet the full GraphQL schema (introspection) for a Polymarket subgraphquery_subgraphExecute a custom GraphQL query against a Polymarket subgraphget_market_dataGet Polymarket market/condition data including outcomes and volumes from the Main subgraphget_account_pnlGet a trader's P&L and performance metrics from the Beefy P&L subgraphget_user_positionsGet a user's current positions from the Slimmed P&L subgraph. Falls back gracefully if indexers are behind.get_recent_activityGet recent splits, merges, and redemptions from the Activity subgraphget_orderbook_tradesGet recent order fills from the Orderbook subgraphget_global_statsGet aggregate Polymarket platform statistics. Combines market counts from the Main subgraph with accurate volume/fee/trade data from the Orderbook subgraph.get_daily_statsGet daily platform statistics from the Beefy P&L subgraph: volume, fees, trader counts, and market activity per day. Use this for trend analysis and historical performance.get_top_tradersGet the top Polymarket traders ranked by realized P&L, win rate, or volume from the Beefy P&L subgraph.get_market_positionsGet the top positions for a specific market token from the Beefy P&L subgraph. Shows who holds the largest positions and their P&L.- +23 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.
Tradememory Protocol
{
"mcpServers": {
"mnemox-ai-tradememory-protocol": {
"command": "uvx",
"args": [
"tradememory-protocol"
]
}
}
}
Graph Polymarket MCP
{
"mcpServers": {
"paulieb14-graph-polymarket-mcp": {
"command": "npx",
"args": [
"-y",
"graph-polymarket-mcp"
],
"env": {
"GRAPH_API_KEY": "YOUR_GRAPH_API_KEY_HERE",
"MCP_HTTP_PORT": "YOUR_MCP_HTTP_PORT_HERE"
}
}
}
}
Frequently asked questions
Tradememory Protocol is categorized under Finance & Fintech and uses a local stdio subprocess. Graph Polymarket MCP belongs to Finance & Fintech and uses a local stdio subprocess. Pick Tradememory Protocol for finance & fintech work and Graph Polymarket MCP 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. Graph Polymarket MCP is listed as free. Auth: api key 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 mnemox-ai-tradememory-protocol and paulieb14-graph-polymarket-mcp 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 4 views, 0 installs and 1,425 GitHub stars. Graph Polymarket MCP has 61/100 (Good) with 2 views, 0 installs and 4 GitHub stars.