US stock market data for AI agents — 23 years of minute bars, SEC filings, fundamentals.
Cabrini Market Data MCP server exists for a simple reason — assistants are far more useful when they can act on Cabrini Market Data directly instead of describing what you should do. US stock market data for AI agents — 23 years of minute bars, SEC filings, fundamentals.
US stock market data for AI agents. 23 years of intraday and daily bars, SEC fundamentals, filings, and insider data — every US equity from 2003 to present.
Once Cabrini Market Data is connected, these are the calls the assistant has available:
Homepage — https://cabrini.aiMethod — Pricecabrini on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Cabrini Market Data's toolset — Homepage, Method — is a fair guide to whether it matches your workflow. It is maintained by ai.cabrini; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Homepage | https://cabrini.ai |
| Method | Price |
{
"mcpServers": {
"cabrini": {
"url": "https://cabrini.ai/mcp"
}
}
}Configuration as documented by the project. Restart the client after saving.
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