mcpField
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Cabrini Market Data

Cabrini Market Data

Remote
github.com/nlapiFinance1Updated 13w ago
Repository

About Cabrini Market Data

Cabrini Market Data provides US equity intraday and daily prices, SEC data, filings, and insider transactions for AI agents. Its Python client supports x402 USDC payments, and its data is also available through an MCP endpoint.

Key features

  1. 01Intraday bars from 2003 onward
  2. 02Daily OHLCV and VWAP
  3. 03SEC company, filing, and fundamentals data
  4. 04Insider transaction data
  5. 05Stock screening and research briefs

Use cases

  1. 01Analyze intraday or historical stock movements
  2. 02Screen US equities against criteria
  3. 03Review company filings and insider transactions

MCP support

The hosted MCP endpoint at https://cabrini.ai/mcp exposes the market data service to MCP clients. Paid requests use x402 USDC payments.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://cabrini.ai/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

15
ParameterTypeRequiredDescription
datestringYesYYYY-MM-DD
tickerstringYesStock ticker (e.g. AAPL)
intervalintegerNoBar interval in minutes: 3, 6, 9, 12, 15, 30, 60, or 240 (default 3)

Details

From the project README

cabrini

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

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