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grahammccain/chart-library-mcp

by Various

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

G

MCP

grahammccain/chart-library-mcp

Added 1 June 2026

#ai-agents #claude #finance #llm-tools #market-data #mcp #mcp-server #model-context-protocol

Overview

MCP server that connects to Chart Library, a visual chart pattern search engine for stock charts. It lets users find similar historical chart patterns and see subsequent price movements.

Best for

Best for
Developers building automated trading or analysis tools who need quick access to historical chart pattern matches.

Use cases

  • Search for historical stock chart patterns that match a current chart
  • Analyze what happened after a given pattern appeared in the past
  • Integrate pattern-based stock analysis into trading workflows via MCP

Notes

MCP server that connects to Chart Library, a visual chart pattern search engine for stock charts. It lets users find similar historical chart patterns and see subsequent price movements.

8 stars on GitHub. Last updated 2026-05-26. Licensed MIT.

Use cases

  • Search for historical stock chart patterns that match a current chart
  • Analyze what happened after a given pattern appeared in the past
  • Integrate pattern-based stock analysis into trading workflows via MCP

Pros

  • Straightforward MCP integration for developers building trading tools
  • Provides historical pattern matching with clear outcome data
  • Lightweight Python implementation with low setup overhead

Cons

  • Relies entirely on Chart Library’s external API and data availability
  • Limited to historical stock data, may not cover all markets or timeframes
  • Low community adoption (8 stars) suggests limited ecosystem or support

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Straightforward MCP integration for developers building trading tools
  • Provides historical pattern matching with clear outcome data
  • Lightweight Python implementation with low setup overhead

Cons

  • Relies entirely on Chart Library's external API and data availability
  • Limited to historical stock data, may not cover all markets or timeframes
  • Low community adoption (8 stars) suggests limited ecosystem or support