cabrini

MCP serverCommerce & finance

Once 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.

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

MethodPriceDescription
query(ticker, date)$0.025Full trading day of intraday bars
daily(ticker, start, end)$0.001/yearDaily OHLCV + VWAP — the absolute prices
batch(tickers, date)$0.02/tickerSeveral tickers, one date, no limit
range(ticker, start, end)$0.01/trading dayMulti-day intraday, no limit
bars(ticker, date, interval)$0.015/dayResampled intraday, 3-240 min
scan(date, **criteria)$0.10Screen every US stock; needs >= 1 criterion
tickers(date)$0.005List traded tickers
company(ticker)$0.005Company profile from SEC EDGAR
fundamentals(ticker)$0.02SEC quarterly data
filings(ticker)$0.01 / $0.05SEC filing index; +extracted section text
insiders(ticker)$0.02Insider transactions (Form 4)
brief(ticker)$0.25Joined 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:

  1. Client sends request → server returns 402 with a PAYMENT-REQUIRED header
  2. Client signs a USDC transfer authorization (EIP-3009)
  3. Client replays request with X-PAYMENT header containing the signed authorization
  4. Cloudflare edge worker verifies signature, submits to Base, forwards to origin
  5. Origin returns data

The Cabrini client handles all of this automatically. You just need a wallet with USDC on Base.

Get USDC on Base

  1. Bridge from Ethereum: bridge.base.org
  2. Buy directly: Coinbase → send USDC to your wallet on Base network
  3. Faucet (testnet): not needed, mainnet USDC is cheap ($0.025/query)

Links

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_bars
  • list_tickers
  • query_range
  • query_batch
  • scan_market
  • query_daily
  • get_brief
  • get_company
  • get_fundamentals
  • get_insiders
  • get_filings
  • get_bars
  • get_sample
  • get_pricing
  • get_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