chart-library-mcp

mcp
Guvenlik Denetimi
Uyari
Health Uyari
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 5 GitHub stars
Code Gecti
  • Code scan — Scanned 10 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This tool is an MCP server that connects AI assistants to a visual chart pattern search engine. It allows users to find similar historical stock charts and analyze what happened next in the market based on 10 years of historical data.

Security Assessment
The overall risk is rated as Low. The code scan evaluated 10 files and found no dangerous patterns, hardcoded secrets, or requests for dangerous local permissions. However, it does require an external API key (`CHART_LIBRARY_API_KEY`) to function. This means it inherently makes network requests to a remote server to query the stock database and fetch financial data. It does not appear to execute arbitrary shell commands or access sensitive local files.

Quality Assessment
The project is actively maintained, with its last push occurring today. It is properly licensed under the standard MIT license, ensuring clear terms of use. The documentation is thorough and provides clear installation instructions for a wide variety of AI platforms, including Claude, Cursor, VS Code, and ChatGPT. The main drawback is its low community visibility; the repository currently has only 5 GitHub stars. While this means it has not been broadly vetted by a large open-source community, the clean code scan and active updates are positive indicators.

Verdict
Safe to use, though developers should be aware it requires passing a third-party API key and makes external network requests.
SUMMARY

MCP server for Chart Library — visual chart pattern search engine. Find similar historical stock charts and see what happened next.

README.md

Chart Library MCP Server

PyPI
License: MIT
Glama Score
Tools

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.


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 -- 24M patterns, 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.


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.

8 Canonical Tools

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.

Typical agent flow

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

Legacy tools (deprecated, still callable)

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)

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.


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

Links


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

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