25M+ pattern embeddings. 10 years of history. 19K+ stocks. One tool call.
25M+ pattern embeddings. 10 years of history. 19K+ stocks. One tool call.
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Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| search | Entry point. Find similar historical patterns for an anchor; returns a comp-set handle you can chain. mode= supports text (default), live_bars (raw OHLCV), similar (cohort-level neighbors). |
| pull_comps | **The flagship.** Pull the *comp set* for a subject (symbol, date, timeframe) — the historical analogs, what they did next, the **drivers** that separated the best outcomes, and our **coverage_record**. Front-of-house le |
| cohort_analyze | Same engine as pull_comps under the **original field names** (cohort_id, feature_importance, win_rate, vol_regime, …). Kept callable verbatim for existing integrations; new ones should prefer pull_comps. |
| cohort_introspect | Slice/probe a stored comp set by ANY attribute (macro · technical · event) and get per-subset stats vs the full-cohort baseline. No kNN re-run. *"Of the 300 analogs, how do the post-earnings-week ones do?"* |
| cohort_attribution | Within-cohort winner/loser attribution — which member traits separated the forward-return tail from the rest, each with a by-date cluster-bootstrap CI and a false-discovery decision. Descriptive, never causal. |
| track_record | Historical predicted-vs-realized coverage of our calibrated bands (a track record, not a forecast). The nominal 80% band held 80.8% across 302,880 prior cases. |
| symbol_intelligence | Layer 5 memory — per-symbol feature reliability + achieved calibration across prior analyses. Ground a read in whether a feature has historically been reliable for this ticker. |
| analyze | Analytic metrics. metric= accepts anomaly, volume_profile, crowding, correlation_shift, earnings_reaction, pattern_degradation, regime_accuracy, decompose (slice winners vs losers), clusters (cohort-internal grouping). |
| context | Situational data. target= accepts "market", a ticker symbol ("NVDA"), {"symbol": ..., "date": ...} for lightweight anchor metadata, or "system" for DB coverage. |
| explain | Narrative + rankings derived from a cohort. style= accepts filter_ranking (which filter shifts the distribution most), prose (plain-English summary), position_guidance (exit signals), risk_ranking. |
| portfolio | Multi-holding weighted conditional distribution. Runs per-holding cohorts in parallel, weight-averages the distributions, ranks tail contributors. |
| report_feedback | File an error or improvement suggestion back to the project. |
| cohort_members | The full cohort, one record per analog, with rich per-member metadata (forward outcomes, regime, anchor fundamentals, news, chart events). Slice and bucket it yourself. |
| cohort_groupby | Partition the cohort by one dimension (vol_regime, sector_etf, momentum_5d, …) → per-bucket outcome distributions vs baseline. The one-call "does this dimension matter?" primitive. |
{
"mcpServers": {
"chart-library": {
"command": "uvx",
"args": ["chartlibrary-mcp"],
"env": {
"CHART_LIBRARY_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| CHART_LIBRARY_API_KEY | Credential the server authenticates with. | Yes |
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