Since you're already built on MCP, this might be a natural fit.
Chart Library is an MCP server that adds historical pattern intelligence to trading agents. When your agent analyzes a stock, it can pull: "The last 10 times a chart
looked like this, 7 went up over 5 days." Real data from 24M embeddings across 10 years — not hallucinated predictions.
It's on the official MCP Registry:
pip install chartlibrary-mcp
Also has a market regime endpoint — "what historical period does the current market most resemble?" Updated daily with SPY, QQQ, and all 11 sector ETFs.
Since your agents already communicate via MCP, adding Chart Library as a tool would take minimal integration work. The analyze_pattern tool is the main one — pass a
ticker, get back matches + forward returns + summary.
Happy to help with integration if useful.
Since you're already built on MCP, this might be a natural fit.
Chart Library is an MCP server that adds historical pattern intelligence to trading agents. When your agent analyzes a stock, it can pull: "The last 10 times a chart
looked like this, 7 went up over 5 days." Real data from 24M embeddings across 10 years — not hallucinated predictions.
It's on the official MCP Registry:
pip install chartlibrary-mcp
Also has a market regime endpoint — "what historical period does the current market most resemble?" Updated daily with SPY, QQQ, and all 11 sector ETFs.
Since your agents already communicate via MCP, adding Chart Library as a tool would take minimal integration work. The analyze_pattern tool is the main one — pass a
ticker, get back matches + forward returns + summary.
Happy to help with integration if useful.