Server data from the Official MCP Registry
23 MCP tools. Context-aware chart pattern intelligence. 24M embeddings, regime-conditioned.
23 MCP tools. Context-aware chart pattern intelligence. 24M embeddings, regime-conditioned.
Valid MCP server (2 strong, 3 medium validity signals). 7 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
5 files analyzed · 8 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: CHART_LIBRARY_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-grahammccain-chart-library": {
"env": {
"CHART_LIBRARY_API_KEY": "your-chart-library-api-key-here"
},
"args": [
"chartlibrary-mcp"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Works with: Claude Desktop | Claude Code | ChatGPT | GitHub Copilot | Cursor | VS Code | Any MCP client
Ask your AI agent "what happened the last 10 times a chart looked like this?" and get a real answer.
24 million pattern embeddings. 10 years of history. 15,000+ stocks. One tool call.
> "What does NVDA's chart look like right now?"
Found 10 similar historical patterns for NVDA (2026-04-04, RTH timeframe):
Closest match: AAPL 2023-05-12 (distance: 0.41)
Forward returns across all 10 matches:
1-day: +0.8% avg (7/10 positive)
5-day: +3.1% avg (8/10 positive)
10-day: +4.7% avg (7/10 positive)
Summary: NVDA's current consolidation near highs mirrors 10 historical
setups, most notably AAPL's May 2023 pre-breakout pattern. 8 of 10
resolved higher within a week, with a median 5-day gain of +2.8%.
No hallucinated predictions. No refusals. Just factual historical data your agent can cite.
pip install chartlibrary-mcp
Download the chart-library-1.1.1.mcpb extension file and open it with Claude Desktop for automatic installation.
claude mcp add chart-library -- chartlibrary-mcp
Add to claude_desktop_config.json:
{
"mcpServers": {
"chart-library": {
"command": "chartlibrary-mcp",
"env": {
"CHART_LIBRARY_API_KEY": "cl_your_key"
}
}
}
}
Add to .cursor/mcp.json or VS Code MCP settings:
{
"servers": {
"chart-library": {
"command": "chartlibrary-mcp",
"env": {
"CHART_LIBRARY_API_KEY": "cl_your_key"
}
}
}
}
Add to .vscode/mcp.json in your project (this file is already included in the chart-library repos):
{
"servers": {
"chart-library": {
"command": "chartlibrary-mcp",
"env": {
"CHART_LIBRARY_API_KEY": "cl_your_key"
}
}
}
}
Copilot Chat will auto-detect the MCP server when you open the project. Use @mcp in Copilot Chat to invoke tools.
ChatGPT connects to MCP servers via remote HTTP endpoints. To set up:
https://chartlibrary.io/mcpNote: The remote endpoint at
https://chartlibrary.io/mcpuses Streamable HTTP transport. If you need SSE fallback, usehttps://chartlibrary.io/mcp/sse.
For any MCP client that supports remote HTTP connections:
https://chartlibrary.io/mcp
This endpoint supports both Streamable HTTP and SSE transports, no local installation required.
Free tier: 200 calls/day, no credit card required. Get an API key at chartlibrary.io/developers or use basic search without one.
> get_exit_signal("TSLA")
Signal: HOLD (confidence: 72%)
Similar patterns that exited early: 3/10 would have avoided a drawdown
Similar patterns that held: 7/10 gained an additional +2.1% over 5 days
Recommendation: Pattern suggests continuation. No exit signal triggered.
> get_sector_rotation()
Leaders (30-day relative strength):
1. XLK Technology +4.2%
2. XLY Cons. Disc. +3.1%
3. XLC Communication +2.8%
Laggards:
9. XLU Utilities -1.4%
10. XLP Cons. Staples -2.1%
11. XLRE Real Estate -3.3%
Regime: Risk-On (growth > defensives)
> run_scenario("AMD", spy_change=-3.0)
When SPY fell ~3%, AMD historically:
Median move: -5.2%
Best case: +1.1%
Worst case: -11.4%
Positive: 18% of the time
AMD shows 1.7x beta to SPY downside moves.
Chart Library 2.0 consolidates 22 legacy tools into 8 composable primitives. Chain them via cohort_id handles for sub-second refinement without re-running kNN.
| Tool | What it does |
|---|---|
search | Entry point. Returns cohort_id + anchor + n_matches for a ticker+date. Feed the handle into cohort, analyze, or explain to chain. |
cohort | The core primitive. Conditional distribution (p10/p25/p50/p75/p90 + calibrated bands + MAE/MFE + hit rates + survivorship) for a chart pattern, filtered by regime/sector/liquidity/event. One call replaces the legacy get_cohort_distribution, refine_cohort_with_filters, run_scenario, and get_regime_win_rates. |
analyze | Analytic metrics via metric= enum: anomaly, volume_profile, crowding, correlation_shift, earnings_reaction, pattern_degradation, regime_accuracy. |
context | Situational data via target=: ticker metadata, market regime + sector rotation, or DB coverage stats. |
explain | Narrative + rankings via style= enum: filter_ranking (which filter shifts the distribution most), prose (plain-English summary), position_guidance (exit signals), risk_ranking (Sharpe-ranked picks). |
portfolio | Portfolio-level conditional distribution across holdings. Weight-averages distributions, ranks tail contributors. |
anchor_fetch | New in 2.0. Lightweight (symbol, date) metadata fetch — sector, market cap, point-in-time regime. Avoids full kNN when you just need context for a ticker. |
report_feedback | Report errors or suggest improvements. |
These tools replace hallucinated "on average this pattern returns X%" with real conditional base rates. See the grounded-base-rates pattern for the full loop.
1. search("NVDA 2024-06-18") → cohort_id
2. cohort(cohort_id=..., filters={regime:{same_vix_bucket: true}})
→ conditional distribution
3. explain(cohort_id=..., style="filter_ranking") → which filter matters most
4. cohort(cohort_id=..., filters={...new filter...}) → refined distribution
For backward compatibility, these 22 legacy tool names remain in place and are marked
deprecated in their MCP annotations. They forward to the canonical tool and will be
removed in a future major release. Migrate via the mapping below:
| Legacy | Replacement |
|---|---|
search_charts, search_batch, get_discover_picks | search |
get_cohort_distribution, refine_cohort_with_filters, run_scenario, get_regime_win_rates, compare_to_peers | cohort |
detect_anomaly, get_volume_profile, get_crowding, get_earnings_reaction, get_correlation_shift, get_pattern_degradation, get_regime_accuracy | analyze (metric=) |
get_sector_rotation, get_status, get_market_context | context |
get_pattern_summary, explain_cohort_filters, get_exit_signal, get_risk_adjusted_picks | explain (style=) |
get_portfolio_health | portfolio |
analyze_pattern, get_follow_through, check_ticker | search + cohort (+ optional explain) |
Chart Library indexes a large library of historical chart patterns and exposes them behind a conditional-distribution API. Every query returns sample sizes, percentiles, and calibrated forward-return bands — never a point forecast.
When your agent calls analyze_pattern("NVDA"), the server:
The result: factual, citation-ready statements like "out of N similar historical patterns, the median 5-day return was X% (80% band [p10, p90])" that your agent can present without hallucinating or hedging.
| Tier | Calls/day | Price |
|---|---|---|
| Sandbox | 200 | Free |
| Builder | 5,000 | $29/mo |
| Scale | 50,000 | $99/mo |
Get your key at chartlibrary.io/developers.
export CHART_LIBRARY_API_KEY=cl_your_key
Chart Library provides historical pattern data for informational purposes. Not financial advice.
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