chartlibrary-mcp 5.1.0

Cohort intelligence engine for stock chart patterns. Anchor any (symbol, date, timeframe) and your AI agent gets the cohort of 300 historical analogs, the full forward-return distribution, and the features that separated winners from losers. 8 composite MCP tools (search, cohort, discover, analyze, context, narrative, explain, portfolio). 25M+ patterns, 19K+ symbols, 10 years. Validated 50–0 in a blind paired AI-agent evaluation.

Chart Library MCP Server

Works with: Claude Desktop | Claude Code | ChatGPT | GitHub Copilot | Cursor | VS Code | Any MCP client

Cohort intelligence engine for stock chart patterns — give your AI agent the cohort of historical analogs, the full forward-return distribution, and the features that separated winners from losers. Calibrated, methodology-honest, no overstated confidence.

📖 What is cohort intelligence? · 🛠️ Full MCP setup guide · 🤖 Build an AI trading agent with Claude

25M+ pattern embeddings. 10 years of history. 19K+ stocks. One tool call.

> "What does NVDA's chart on 2024-08-05 1h look like historically?"

NVDA · 2024-08-05 · 1h — cohort of 500 historical analogs
(485 with realized 5-day returns)

  Distribution at 5 days forward:
    median:        −1.3%
    p10 ·· p90:    −11.3% ·· +6.8%   (80% empirical band)
    win rate:      44%
    cohort_score:  0.31 (modest)

  Features that separated winners from losers:
    + credit_spread_state = tight
    + macro_state = bullish
    + pct_off_52w_low (further off)
    − vol_regime = low

  Summary: NVDA's 1-hour pattern on 2024-08-05 has 500 historical
  analogs. The cohort's 5-day distribution is bearish-leaning
  (median −1.3%, win rate 44%) — the historical record does NOT
  show this pattern typically resolving bullish. Conditioning on
  tight credit spreads and a bullish macro state would have
  separated the outperformers within the cohort.

A retrieval, not a forecast. No hallucinated predictions. No cherry-picking. Just the empirical record your agent can cite.

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Quick Start

pip install chartlibrary-mcp

Claude Desktop (One-Click Install)

Download the chart-library-1.1.1.mcpb extension file and open it with Claude Desktop for automatic installation.

Claude Code

claude mcp add chart-library -- chartlibrary-mcp

Claude Desktop (Manual)

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "chart-library": {
      "command": "chartlibrary-mcp",
      "env": {
        "CHART_LIBRARY_API_KEY": "cl_your_key"
      }
    }
  }
}

Cursor / VS Code

Add to .cursor/mcp.json or VS Code MCP settings:

{
  "servers": {
    "chart-library": {
      "command": "chartlibrary-mcp",
      "env": {
        "CHART_LIBRARY_API_KEY": "cl_your_key"
      }
    }
  }
}

GitHub Copilot (VS Code)

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 (Developer Mode)

ChatGPT connects to MCP servers via remote HTTP endpoints. To set up:

1. Enable Developer Mode: Go to ChatGPT Settings > Apps > Advanced settings > Developer mode (requires Pro, Plus, Business, Enterprise, or Education plan)
2. Create a connector: In Settings > Connectors, click Create and enter:
   • Name: Chart Library
   • Description: Historical chart pattern search engine — 25M+ patterns across 19K+ stocks, 10 years of data
   • URL: https://chartlibrary.io/mcp
   • Authentication: No Authentication (or OAuth if using an API key)
3. 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/mcp uses Streamable HTTP transport. If you need SSE fallback, use https://chartlibrary.io/mcp/sse.

Remote MCP Endpoint

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.

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What Can Your Agent Do With This?

"Should I be worried about my TSLA position?"

> 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.

"What sectors are rotating in right now?"

> 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)

"What happens to AMD if SPY drops 3%?"

> 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.

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8 Canonical Tools

Chart Library v5 ships a clean 8-tool surface. Chain them via cohort_id handles for sub-second refinement without re-running kNN.

Tool	What it does
search	Entry point. Find similar historical patterns for an anchor; returns a cohort_id you can chain. mode= supports text (default), live_bars (raw OHLCV), similar (cohort-level neighbors).
cohort	The core primitive. Conditional distribution analysis. depth="basic" returns kNN + outcome distribution; depth="full" adds Layer 3 feature importance + regime stratification + risk profile; depth="compare" pits two anchors side-by-side. Filters across regime / sector / liquidity / event.
discover	What's interesting today. mode="picks" (cohort-ranked top picks), mode="daily_setups" (pre-enriched briefs in one call), mode="risk_adjusted" (Sharpe-ranked).
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.
narrative	News intelligence. mode="pulse" (single-symbol narrative-change score + FinBERT sentiment) or mode="alerts" (market-wide divergence anomalies).
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 analysis OR per-symbol track record. mode="basic" (multi-holding weighted cohort) or mode="symbol_intel" (per-symbol Layer 5 memory).

Plus report_feedback for filing errors / suggestions back to the project.

These tools replace hallucinated "on average this pattern returns X%" with real conditional base rates. The full distinction — what they do and how to read responses — is documented at /concepts/cohort-intelligence and /concepts/reading-a-cohort-response.

Typical agent flow

1. search(query="NVDA 2024-06-18")                    → cohort_id
2. cohort(symbol="NVDA", date="2024-06-18", depth="full",
          filters={"vol_regime": ["high"]})
                                                       → Layer 3 distribution + features
3. explain(cohort_id=..., style="filter_ranking")     → which filter matters most
4. cohort(symbol=..., date=..., depth="full",
          filters={...refined...})                    → re-conditioned distribution

Migrating from v4 / v3 / v2

v5 reduces the surface from 19 active tools to 8 composite tools. Twelve previously-active tools (cohort_analyze, cohort_compare, decompose, clusters, live_search, similar_cohorts, symbol_intelligence, anchor_fetch, narrative_pulse, narrative_alerts, discover_picks, get_daily_setups) are retained as DEPRECATED wrappers that forward to the canonical tools — v4 callers keep working unchanged. New agents should reach for the 8 canonical tools.

The v3-era tools (search_charts, get_cohort_distribution, etc.) have been removed in v5. If your code still calls them, pin chartlibrary-mcp<5.0.0 until you migrate to the canonical surface. The mapping:

Legacy (removed in v5)	Replacement
search_charts, search_batch, get_discover_picks	search / discover
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)

Previously active in v4 (now DEPRECATED in v5)	Replacement
cohort_analyze	cohort(depth="full")
cohort_compare	cohort(depth="compare", compare_with={...})
decompose, clusters	`analyze(metric="decompose"
live_search, similar_cohorts	`search(mode="live_bars"
symbol_intelligence	portfolio(mode="symbol_intel")
anchor_fetch	context(target={"symbol": ..., "date": ...})
narrative_pulse, narrative_alerts	`narrative(mode="pulse"
discover_picks, get_daily_setups	`discover(mode="picks"

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How It Works

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:

1. Builds a representation of NVDA's current chart state
2. Retrieves historically similar patterns
3. Looks up what happened over the following 1, 3, 5, and 10 days
4. Returns the distribution + a plain-English summary via Claude Haiku

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.

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API Key

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

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Links

• Website
• API Documentation
• Developer Portal
• Regime Tracker
• Python SDK | JavaScript SDK

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Chart Library provides historical pattern data for informational purposes. Not financial advice.