25M+ pattern embeddings. 10 years of history. 19K+ stocks. One tool call.
Most knowledge and memory work still happens through a UI a human drives. Chart Library MCP MCP server moves it into the conversation instead. 25M+ pattern embeddings. 10 years of history. 19K+ stocks. One tool call.
The server publishes 14 tools. What each one is for:
search — Entry point. Find similar historical patterns for an anchor; returns a comp-set handle you can chain. mode= supports text (default), live_bars (rawpull_comps — The flagship. Pull the comp set for a subject (symbol, date, timeframe) — the historical analogs, what they did next, the drivers thatcohort_analyze — Same engine as pull_comps under the original field names (cohort_id, feature_importance, win_rate, vol_regime, …). Kept callable verbatim forcohort_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. *"Ofcohort_attribution — Within-cohort winner/loser attribution — which member traits separated the forward-return tail from the rest, each with a by-date cluster-bootstraptrack_record — Historical predicted-vs-realized coverage of our calibrated bands (a track record, not a forecast). The nominal 80% band held 80.8% across 302,880symbol_intelligence — Layer 5 memory — per-symbol feature reliability + achieved calibration across prior analyses. Ground a read in whether a feature has historicallyanalyze — Analytic metrics. metric= accepts anomaly, volume_profile, crowding, correlation_shift, earnings_reaction, pattern_degradation, regime_accuracycontext — Situational data. target= accepts "market", a ticker symbol ("NVDA"), {"symbol": ..., "date": ...} for lightweight anchor metadata, or "system" forexplain — Narrative + rankings derived from a cohort. style= accepts filter_ranking (which filter shifts the distribution most), prose (plain-English summary)portfolio — Multi-holding weighted conditional distribution. Runs per-holding cohorts in parallel, weight-averages the distributions, ranks tail contributorsreport_feedback — File an error or improvement suggestion back to the projectBecause this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
Configuration is passed through the environment: CHART_LIBRARY_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Chart Library MCP's toolset — search, pull_comps, cohort_analyze and 11 more — is a fair guide to whether it matches your workflow. It is maintained by grahammccain; 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 |
|---|---|
| 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 |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.