Agent Connectors

Overview

Your AI assistant can use our official connectors to access real-time financial data — stock prices, financials, SEC filings, news, and more.

Connect your client

Pick your client and follow the steps. Interactive clients sign you in automatically — no API key needed.

Install the official Financial Datasets connector from Claude’s Connectors Directory. Works for both Claude.ai (web) and the Claude Desktop app.

Open Financial Datasets in Claude, or search for Financial Datasets in Customize → Connectors
Click Connect and complete sign-in
In any new chat, click + and enable Financial Datasets under Connectors

Claude Code

Run this in your terminal:

claude mcp add --transport http financial-datasets https://mcp.financialdatasets.ai/

Then type /mcp inside Claude Code and complete sign-in in your browser. Verify anytime with claude mcp list.

Install the official Financial Datasets plugin from ChatGPT’s Plugin Directory.

Open Financial Datasets in ChatGPT, or search for Financial Datasets in the Plugin Directory
Click Install plugin, connect the Financial Datasets app, and complete sign-in
In a new chat, mention @Financial Datasets or select it from + → More

Codex

Run these commands in your terminal:

codex mcp add financial-datasets --url https://mcp.financialdatasets.ai/
codex mcp login financial-datasets

Complete sign-in in your browser. The connector is then available in the Codex app, CLI, and IDE extension. Verify anytime with codex mcp list or type /mcp in Codex.

Add Financial Datasets as a custom app in the Gemini web app. Custom apps need a personal Google Account (18 or over, in the US) with Keep Activity turned on.

On your computer, go to gemini.google.com
At the bottom, click Settings → Connected Apps. If you don’t see it, first click Personal Intelligence → Connected Apps
Under Custom apps, find the Add a custom app link to get started field, or click Add a custom app if you already connected one
Enter the MCP connector URL:

https://mcp.financialdatasets.ai/

Click Next, then follow the on-screen instructions to complete sign-in
In a chat, enter @ and select the app to make sure Gemini uses it for your prompt

See Google’s custom apps help page for managing or disconnecting the app.

Add Financial Datasets as a custom connector in Grok.

Go to grok.com/connectors
Click New Connector, then select Custom
Enter the MCP connector URL and complete sign-in:

https://mcp.financialdatasets.ai/

On Grok Business and Enterprise plans, a team admin adds it instead: on the connectors page, click + Add Connector, select Other, and enter the same URL.See xAI’s connectors docs for details.

Add the connector to ~/.hermes/config.yaml:

mcp_servers:
  financial-datasets:
    url: "https://mcp.financialdatasets.ai/"
    auth: oauth

Then run OAuth sign-in from a fresh terminal (don’t start it from inside an already-running Hermes session — the auto-reload timeout is too short for browser auth):

hermes mcp login financial-datasets

Complete sign-in in your browser. In an active Hermes session, reload MCP tools with /reload-mcp, or start a new chat:

hermes chat

See Hermes’ MCP docs for filtering, re-auth, and troubleshooting.

Add the Financial Datasets MCP connector to OpenClaw, then complete OAuth sign-in:

openclaw mcp add financial-datasets \
  --url https://mcp.financialdatasets.ai/ \
  --transport streamable-http \
  --auth oauth

openclaw mcp login financial-datasets

OpenClaw prints an authorization URL — approve in your browser, then finish with:

openclaw mcp login financial-datasets --code <code>

Verify the connection anytime with:

openclaw mcp probe financial-datasets

Prefer editing config by hand? Add this under mcp.servers in ~/.openclaw/openclaw.json, then run the same openclaw mcp login flow:

{
  "mcp": {
    "servers": {
      "financial-datasets": {
        "url": "https://mcp.financialdatasets.ai/",
        "transport": "streamable-http",
        "auth": "oauth"
      }
    }
  }
}

See OpenClaw’s MCP docs for full CLI and config reference.

Add the following to your ~/.cursor/mcp.json file, then save and restart Cursor:

{
  "mcpServers": {
    "financial-datasets": {
      "url": "https://mcp.financialdatasets.ai/"
    }
  }
}

Cursor automatically detects all available tools and calls the relevant one when you ask financial data questions in chat.

Programmatic access

Building a script or a production agent? Connect to the https://mcp.financialdatasets.ai/api endpoint with an API key (sign up free, then generate a key in your dashboard).

Python
Claude Managed Agents

For scripts, notebooks, and custom MCP integrations, connect to the /api endpoint with your API key.Install the MCP Python SDK:

pip install mcp

Connect, then call a tool — here we fetch the latest price for Apple:

import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

API_KEY = "your-api-key"
SERVER_URL = "https://mcp.financialdatasets.ai/api"

async def main():
    # Open an authenticated connection to the MCP connector
    async with streamablehttp_client(
        SERVER_URL,
        headers={"X-API-KEY": API_KEY},
    ) as streams:
        read_stream, write_stream, _ = streams

        # Start an MCP session
        async with ClientSession(read_stream, write_stream) as session:
            await session.initialize()

            # Call any tool from the MCP Tools list
            result = await session.call_tool(
                "get_stock_price",
                {"ticker": "AAPL"},
            )
            print(result.content)

asyncio.run(main())

For production agents built on Anthropic’s hosted Managed Agents platform, authenticate with your API key using a static_bearer vault credential.

Generate an API key from your account dashboard.
Create a static_bearer vault credential pointing at our /api endpoint:

{
  "type": "static_bearer",
  "mcp_server_url": "https://mcp.financialdatasets.ai/api",
  "token": "your-api-key"
}

Reference the vault ID when creating a session. Anthropic injects Authorization: Bearer <your-api-key> on every MCP call.

See Anthropic’s vault credentials docs for full SDK examples in Python, TypeScript, Go, and Java.

Available tools

Browse all 20+ tools

Beneficial Ownership

get_beneficial_owners: List beneficial owners (holders of more than 5% of a company’s shares, from SEC Schedules 13D/13G) with their filer CIK and name. Optionally filter by name prefix. Use this to discover the filer_cik to pass to get_beneficial_ownership.
get_beneficial_ownership: Get 5%+ beneficial-ownership stakes from SEC Schedules 13D and 13G. Query by ticker (who owns this company) or by filer_cik (what stakes does this owner hold). Use type=activist for Schedule 13D activist stakes or type=passive for 13G stakes; history=true returns each stake’s full amendment chain instead of its current state.

Company Information

get_company_facts: Get company details including employee count, sector, industry, exchange, and more.

Earnings

get_earnings: Get earnings data from SEC filings. Both modes return a flat list of EarningsRecord entries — same shape in either mode. With ticker, returns the most recent SEC filings (8-K / 10-Q / 10-K / 20-F) for that company. Without ticker, returns the most recently filed earnings across all covered companies (the real-time feed). Each entry exposes report_period, source_type, filing_date, accession_number, plus quarterly and/or annual financial blocks.

Financial Metrics

get_financial_metrics: Get historical financial metrics such as P/E ratio, enterprise value, and revenue per share.
get_financial_metrics_snapshot: Get a snapshot of the latest financial metrics, including market cap, P/E ratio, and dividend yield.

Financial Statements

get_income_statement: Get historical income statement data (revenue, expenses, net income).
get_balance_sheet: Get historical balance sheet data (assets, liabilities, equity).
get_cash_flow_statement: Get historical cash flow statement data (operating, investing, financing activities).

Each of these tools accepts an as_reported flag. Set as_reported: true to return the raw line items exactly as they appear in the company’s filing, instead of our standardized fields. As-reported data supports only annual and quarterly periods (a ttm period is treated as annual).Index Funds

get_index_fund: Get an ETF or index fund’s holdings and each position’s weight (percent of net assets) for a fund ticker (e.g., SPY). Returns the fund’s latest filing by default, or the composition as of a past date via as_of; optionally filter by asset_class.

Insider Ownership

get_insider_ownership: Get insider ownership statements for a company: what officers, directors, and 10% owners actually hold (common shares, options, RSUs), from SEC Forms 3 and 5. Complements get_insider_trades — trades are the events, ownership statements are the state. Filter by insider name or form_type (3 for initial statements, 5 for annual statements).

Insider Trades

get_insider_trades: Get insider trading transactions for a company, including purchases, sales, and other transactions by officers, directors, and major shareholders.

Institutional Holdings

get_institutional_investors: List institutional investors (SEC 13F filers) with their CIK and most recent reported name. Optionally filter by case-insensitive name prefix to discover the filer_cik to pass to get_institutional_holdings.
get_institutional_holdings: Get SEC 13F institutional holdings sourced directly from EDGAR. Query by filer_cik (what positions a filer holds) or by ticker (which institutional filers hold this security). Defaults to the latest available quarter; optional report_period, report_period_gte, and report_period_lte filters narrow the range.

Interest Rates

get_interest_rates: Get the latest policy interest rates from major central banks (e.g., FED, ECB, BOE, BOJ). Returns a snapshot of each bank’s current rate — no parameters required.

Key Performance Indicators

get_kpi_guidance: Get forward-looking KPI guidance items issued by management in earnings releases and filings. Filter by ticker, period, metric name, and date range.
get_kpi_metrics: Get historical KPI taxonomy metrics extracted from SEC filings (e.g., subscriber counts, ARPU, units sold). Filter by ticker, period (quarterly/annual), metric name, and date range.
get_kpi_non_gaap: Get non-GAAP KPIs reported by companies (e.g., Adjusted EBITDA, Free Cash Flow as defined by the company). Filter by ticker, period, metric name, and date range.

Macro Data

get_macro_data: Get macroeconomic datasets. dataset=yield_curve returns the daily US Treasury par yield curve, dataset=interest_rates returns central bank policy rates (current snapshot, or one bank’s history via bank), dataset=inflation returns US CPI series, and dataset=labor returns US labor market series (payrolls, unemployment, earnings, job openings, weekly jobless claims). Omit the dates for the latest values, or pass start_date / end_date for history.

News

get_news: Get recent news articles for a specific company or the broad market. Pass a ticker for company-specific news, or omit the ticker for general market news.

SEC Filings

get_filings: Get a list of historical SEC filings for a company (10-K, 10-Q, 8-K, and more).
get_filing_items: Extract specific sections from 10-K, 10-Q, and 8-K filings (e.g., Risk Factors, MD&A).
list_filing_item_types: Get a list of all extractable items for 10-K, 10-Q, and 8-K filings.

Segmented Financials

get_segmented_financials: Get segment breakdowns from all three financial statement types (income statement, balance sheet, cash flow) in a single call. Returns revenue, operating income, and depreciation by product/segment; assets, goodwill, and long-lived assets by segment; and capital expenditure by segment.

Stock Prices

get_stock_prices: Get historical stock price data (open, high, low, close, volume) over a date range with configurable intervals.
get_stock_price: Get the latest price snapshot for a stock, including current price and OHLCV data.

Stock Screener

screen_stocks: Screen and filter stocks by financial metrics, valuation ratios, and company attributes. Combine multiple conditions to find stocks matching your criteria (e.g., revenue > $1B, P/E < 20, sector = “Technology”).
list_stock_screener_filters: Get a list of all available filter fields and operators for the stock screener, grouped by category.

Example prompts

After connecting, try asking your AI assistant questions like:

“What is Apple’s current P/E ratio and market cap?”
“Show me Tesla’s income statement for the last 4 quarters”
“Screen for technology stocks with a P/E under 20 and revenue over $1 billion”
“Find the Risk Factors section from Microsoft’s latest 10-K”
“Are there any activist investors in BlackBerry?”
“What companies just reported earnings today?”

Overview

Integrations

Search

Datasets

Guides

Overview

Connect your client

Claude Code

Codex

Programmatic access

Available tools

Example prompts

​Overview

​Connect your client

​Claude Code

​Codex

​Programmatic access

​Available tools

​Example prompts

Overview

Connect your client

Claude Code

Codex

Programmatic access

Available tools

Example prompts