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-mcpDownload the chart-library-1.1.1.mcpb extension file and open it with Claude Desktop for automatic installation.
claude mcp add chart-library -- chartlibrary-mcpAdd 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:
- Enable Developer Mode: Go to ChatGPT Settings > Apps > Advanced settings > Developer mode (requires Pro, Plus, Business, Enterprise, or Education plan)
- Create a connector: In Settings > Connectors, click Create and enter:
- Name: Chart Library
- Description: Historical chart pattern search engine -- 24M patterns, 10 years of data
- URL:
https://chartlibrary.io/mcp - Authentication: No Authentication (or OAuth if using an API key)
- Use in conversations: Select "Developer mode" from the Plus menu, choose the Chart Library app, and ask questions like "What does NVDA's chart look like historically?"
Note: 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.
| Tool | What it does |
|---|---|
analyze_pattern |
Full analysis in one call: search + returns + AI summary |
search_charts |
Find the 10 most similar historical patterns for any ticker |
get_follow_through |
1/3/5/10-day forward returns from matches |
get_pattern_summary |
Plain-English AI summary of pattern implications |
get_discover_picks |
Today's top patterns ranked by interest score |
search_batch |
Analyze up to 20 symbols in parallel |
get_status |
Database coverage and health stats |
| Tool | What it does |
|---|---|
detect_anomaly |
Is this pattern unusual vs the stock's own history? |
get_volume_profile |
Intraday volume breakdown vs historical norms |
get_sector_rotation |
Sector leadership rankings with regime classification |
get_crowding |
Signal crowding: are too many stocks pointing the same way? |
get_earnings_reaction |
How has this stock historically reacted to earnings? |
get_correlation_shift |
Stocks breaking from their usual SPY correlation |
run_scenario |
Conditional returns: "what if the market does X?" |
| Tool | What it does |
|---|---|
get_regime_win_rates |
Win rates filtered by current VIX/yield regime |
get_pattern_degradation |
Are signals losing edge vs historical accuracy? |
get_exit_signal |
Should you hold or exit based on pattern data? |
get_risk_adjusted_picks |
Sharpe-ranked picks from today's pattern scan |
| Tool | What it does |
|---|---|
report_feedback |
Report errors or suggest improvements |
Chart Library uses 24 million pre-computed pattern embeddings (multi-channel numerical encodings of price, volume, volatility, and VWAP) indexed with pgvector for sub-10ms similarity search.
When your agent calls analyze_pattern("NVDA"), the server:
- Computes NVDA's current embedding from the latest market data
- Finds the 10 nearest neighbors by L2 distance across all stocks and dates
- Looks up what happened 1, 3, 5, and 10 days after each historical match
- Generates a plain-English summary via Claude Haiku
The result: factual, citation-ready statements like "8 of 10 similar patterns gained over 5 days" 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_keyChart Library provides historical pattern data for informational purposes. Not financial advice.