cabrini
MCP serverCommerce & financeOnce added, your AI can work with 23 years of US stock market data, including intraday and daily prices and SEC filings. It can research companies, check historical prices, and pull filing details whenever you ask. Payments for data access are made in USDC through x402.
Available today. Use it from your connected AI after setup.
No other account needed.
Add market-data, then ask your AI for the prices or filings you need. Data access is paid in USDC via x402.
Then ask your AI: use cabrini to query minute bars
What your AI can do with it
- Look up intraday and daily prices for US stocks going back 23 years
- Pull historical price data to support analysis and research
- Read SEC filings for US companies
- Combine price history and filings to research a company in one place
- Pay for data access in USDC through x402
From the project's README
As published by nlapi/cabrini-py in README.md.
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.
Pay per query with USDC on Base (x402). No API keys, no subscriptions, no signup.
Install
pip install cabrini
Quick start
from cabrini import Cabrini
c = Cabrini(private_key="0x...") # any Base wallet with USDC
# Intraday bars (pct from daily open) — $0.025
bars = c.query("AAPL", "2024-01-15")
# Daily OHLCV + VWAP (absolute prices) — $0.001/year
daily = c.daily("TSLA", "2024-01-01", "2024-03-31")
# SEC fundamentals — $0.02
fins = c.fundamentals("NVDA")
# Full research brief — $0.25
brief = c.brief("MSFT")
LangChain
from cabrini import get_langchain_tools
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
tools = get_langchain_tools(private_key="0x...")
agent = create_react_agent(ChatOpenAI(model="gpt-4o"), tools)
result = agent.invoke({"messages": [
{"role": "user", "content": "What was NVDA's trading volume on the day of their last earnings?"}
]})
CrewAI
from cabrini import get_crewai_tools
from crewai import Agent, Task, Crew
tools = get_crewai_tools(private_key="0x...")
analyst = Agent(
role="Financial Analyst",
goal="Analyze stock performance using real market data",
tools=tools,
)
task = Task(
description="Compare AAPL and MSFT intraday volatility on 2024-06-15",
agent=analyst,
)
Crew(agents=[analyst], tasks=[task]).kickoff()
MCP (Claude, Cursor, etc.)
Point any MCP client at https://cabrini.ai/mcp:
{
"mcpServers": {
"cabrini": {
"url": "https://cabrini.ai/mcp"
}
}
}
All endpoints
| Method | Price | Description |
|---|---|---|
query(ticker, date) | $0.025 | Full trading day of intraday bars |
daily(ticker, start, end) | $0.001/year | Daily OHLCV + VWAP — the absolute prices |
batch(tickers, date) | $0.02/ticker | Several tickers, one date, no limit |
range(ticker, start, end) | $0.01/trading day | Multi-day intraday, no limit |
bars(ticker, date, interval) | $0.015/day | Resampled intraday, 3-240 min |
scan(date, **criteria) | $0.10 | Screen every US stock; needs >= 1 criterion |
tickers(date) | $0.005 | List traded tickers |
company(ticker) | $0.005 | Company profile from SEC EDGAR |
fundamentals(ticker) | $0.02 | SEC quarterly data |
filings(ticker) | $0.01 / $0.05 | SEC filing index; +extracted section text |
insiders(ticker) | $0.02 | Insider transactions (Form 4) |
brief(ticker) | $0.25 | Joined research brief |
Prices are quoted live in each 402 response and the client pays whatever the server
asks — this table is documentation, not the source of truth.
Output format
Intraday methods (query, range, batch, bars) return fractional change from the
daily open, not price levels:
{"window_start": "2024-01-02T14:30:00", "timestamp": 1704204600000000000,
"pct_open": 0.0, "pct_high": 0.0012, "pct_low": -0.0003, "pct_close": 0.0008,
"volume": 47000, "transactions": 312}
pct_x = (bar_x - day_open) / day_open, so 0.0012 is +0.12%.
daily() carries the absolute levels — open, high, low, close, volume, transactions and
VWAP. Combine the two to reconstruct prices:
day = c.daily("AAPL", "2024-01-02", "2024-01-02")["data"][0]
bars = c.query("AAPL", "2024-01-02")["data"]
close_price = day["open"] * (1 + bars[-1]["pct_close"])
Use daily() rather than a third-party open: our reference is the first bar of the
session and includes pre-market, so an external 09:30 open will not reconcile exactly.
How payment works
Every paid request uses x402 — an open protocol for HTTP micropayments:
- Client sends request → server returns
402with aPAYMENT-REQUIREDheader - Client signs a USDC transfer authorization (EIP-3009)
- Client replays request with
X-PAYMENTheader containing the signed authorization - Cloudflare edge worker verifies signature, submits to Base, forwards to origin
- Origin returns data
The Cabrini client handles all of this automatically. You just need a wallet with USDC on Base.
Get USDC on Base
- Bridge from Ethereum: bridge.base.org
- Buy directly: Coinbase → send USDC to your wallet on Base network
- Faucet (testnet): not needed, mainnet USDC is cheap ($0.025/query)
Links
- Homepage: https://cabrini.ai
- API docs: https://cabrini.ai/docs
- Agent guide: https://cabrini.ai/agents
- MCP endpoint: https://cabrini.ai/mcp
Tools it offers (15)
What this server listed when ahel dialed its public endpoint in Sep 2026, with no key and no account of yours. The names are the server’s own.
query_minute_barslist_tickersquery_rangequery_batchscan_marketquery_dailyget_briefget_companyget_fundamentalsget_insidersget_filingsget_barsget_sampleget_pricingget_stats
Signals
- GitHub stars
- 1
- Last commit
- Jul 2026
Advanced
- Delivery
- market-data MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
- Catalog kind
- mcp-server
- Gateway key
ai-cabrini-market-data- Source
- github.com/nlapi/cabrini-py
- Hosted endpoint
https://cabrini.ai/mcp