Token-efficient semantic compression for numerical data. 95%+ token reduction.
Token-efficient semantic compression for numerical data. 95%+ token reduction. That is what the semantic mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
Semantic Frame converts raw numerical data (NumPy, Pandas, Polars) into natural language descriptions optimized for LLM consumption. Instead of sending thousands of data points to an AI agent, send a 50-word semantic summary.
semantic-frame on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
The server publishes 11 tools. What each one is for:
describe_drawdown — Equity curve drawdown analysis with severitydescribe_trading_performance — Win rate, Sharpe, profit factor metricsdescribe_rankings — Multi-agent/strategy comparisondescribe_anomalies — Enhanced anomaly detection with PnL contextdescribe_windows — Multi-timeframe trend alignmentdescribe_regime — Market regime detection (bull/bear/sideways)describe_allocation — Portfolio allocation suggestions ⚠️LangChain — The LangChain tool exposed by this serverCrewAI — The CrewAI tool exposed by this serverFeatures — The Features tool exposed by this serverSemanticResult — Full analysis result with: - narrative: Human-readable text description - trend: TrendState enum - volatility: VolatilityState enum - data_qualityThis sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Semantic's toolset — describe_drawdown, describe_trading_performance, describe_rankings and 8 more — is a fair guide to whether it matches your workflow. It is maintained by Anarkitty1; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| describe_drawdown | Equity curve drawdown analysis with severity |
| describe_trading_performance | Win rate, Sharpe, profit factor metrics |
| describe_rankings | Multi-agent/strategy comparison |
| describe_anomalies | Enhanced anomaly detection with PnL context |
| describe_windows | Multi-timeframe trend alignment |
| describe_regime | Market regime detection (bull/bear/sideways) |
| describe_allocation | Portfolio allocation suggestions ⚠️ |
| LangChain | The LangChain tool exposed by this server. |
| CrewAI | The CrewAI tool exposed by this server. |
| Features | The Features tool exposed by this server. |
| SemanticResult | Full analysis result with: - narrative: Human-readable text description - trend: TrendState enum - volatility: VolatilityState enum - data_quality: DataQuality enum - anomaly_state: AnomalyState enum - anomalies: List of |
{
"mcpServers": {
"semantic-frame": {
"command": "uvx",
"args": ["semantic-frame"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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