What data is filing-backed versus market snapshot data?

Income statement, balance sheet, cash flow, dividend, and filing endpoints are backed by SEC filing-derived records for U.S. listed issuers. Quote and price endpoints provide structured market snapshots and historical series for application workflows.

Which AI clients support the ApiFinance AI MCP server?

The remote MCP server works with clients that support OAuth and remote MCP, including Claude Desktop, Claude web, Claude Code, Cursor, and custom MCP-compatible agent runtimes.

Is there an official Python SDK?

Yes. ApiFinance AI provides an official Python SDK with methods such as search, get_profile, get_quote, get_income_statements, get_balance_sheets, get_cash_flow_statements, and get_sec_filings.

How far back does historical fundamental coverage go?

ApiFinance AI supports up to 30+ years of financial history where filings are available, allowing long-horizon screening, trend analysis, and model backtesting.

Where can I review data provenance and coverage limits?

Start with the data provenance and data coverage documentation to review supported exchanges, filing-backed source trails, update cadence, and field normalization details before deployment.

ApiFinance AI

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The Financial Data Layer for AI Agents & LLMs | MCP Native API

Financial data API for AI agents and LLM applications. Access company profiles, 30+ years of fundamentals, SEC filings, and MCP-native workflows without stitching together multiple vendors.

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Build AI Financial Workflows Faster

Connect AI agents to structured financial data, then turn that data into research summaries, dashboards, and internal tools without building extra parsing layers.

Investment Research
Get company financials, ratios, and metrics for deep research workflows and analyst copilots.
AI Agent Tools
Build smarter agents that can analyze markets, summarize filings, and answer with grounded data.
Quant Analysis
Access clean historical datasets for screeners, backtests, and systematic financial products.

Connect AI Agents with MCP

Use Model Context Protocol (MCP) to let your AI agents access financial data securely. Structured JSON responses, clear endpoint boundaries, and OAuth-ready flows keep the integration predictable and compact for LLM reasoning.

Plug and play with AI agents
Connect to any agent runtime that supports MCP, including Claude, OpenAI, Cursor, and custom setups.
Structured JSON responses
Get clean, consistent, and compact JSON that is ready for downstream LLM reasoning without brittle parsing or scraping.
Built-in support for research workflows
Use MCP to power agent-assisted analysis, dashboards, and research loops without building extra orchestration layers.

Open MCP guide

Live MCP Demo

Agent asks for research, MCP tools fetch the data, then the answer is composed from structured results.

30+ Years of Financial Data You Can Trust

Explore decades of structured fundamentals, market data, and SEC filing metadata built for research teams, financial products, and AI applications.

30+ Years of History
Long historical coverage since early 1990s.
20,000+ Companies
Global company data across major exchanges.
10M+ Data Points
Updated daily with structured delivery.

Annual

Quarterly

Show YoY

Name
Revenue
Cost Of Goods Sold
Gross Profit
Gross Margin %
Research Development
Selling General Admin Expense
Depreciation Depletion Amortization
Other Operating Expense
Total Operating Expense
Operating Income
Operating Margin
Interest Income
Interest Expense
Other Income Expense
Other Income Minority Interest
Other Net Income Loss
Ebit
Ebitda
Ebitda Margin
Pretax Income
Tax Provision
Tax Rate
Net Income Continuing Operations
Net Income Discontinued Operations
Net Income
Net Income Including Noncontrolling Interests
Is Preferred Dividends
Shares Outstanding
Eps Basic
Eps Diluted
Net Margin

Daily Price History for Market Analysis and Agent Workflows

Daily open, high, low, close, and volume data for AAPL rendered directly from the market data endpoint.

Date	Open	High	Low	Close
No data

SEC Forms for Research and Compliance Workflows

Access structured SEC filing metadata across major form types, including annual reports, quarterly reports, current events, proxy materials, and registration statements. Use it to power compliance checks, filing timelines, governance workflows, and agent-assisted document retrieval.

Broad form coverage
Work with forms like 10-K, 10-Q, 8-K, DEF 14A, S-1, and more through one consistent API pattern.
Timeline-ready metadata
Each filing includes the form type, filing date, accession number, and source URL for downstream workflows.
Useful for agents
Agents can filter form types, find the latest filing, and hand off URLs into retrieval or summarization pipelines.

Date	Form	Accession	Link

Why teams trust the dataset

Start with the provenance, coverage, and support details buyers usually need before they put financial data into production.

Provenance

See where the data comes from

Review how ApiFinance AI links normalized fields back to source filings and market datasets before you build research or compliance workflows.

Review data provenance

Coverage

Verify what the API covers

Check endpoint categories, historical ranges, and supported workflows before you commit engineering time to an integration.

Review data coverage

Support

Talk to the team before rollout

Use the contact page for enterprise questions, source validation, onboarding help, or contract discussions before you ship to production.

Contact ApiFinance AI

Start Building in Minutes

Use the official Python SDK to make a working request in minutes, then scale into scripts, services, and internal tools.

quickstart.py

from apifinance import ApiFinanceClient

Runtime

Interpreter

Python SDK

Mode

Autocomplete + live typing

Hint

Typed snippets mirror real SDK calls

View Python SDK

from apifinance import ApiFinanceClient

client = ApiFinanceClient(
    api_key="your_api_key",
)

# Predictable and compact JSON for LLM reasoning
search = client.search(query="nvidia", limit=10)
profile = client.get_profile("AAPL")
quote = client.get_quote("AAPL")
dividend = client.get_dividend("AAPL")

# Compare annual and quarterly fundamentals for AI agent workflows
income = client.get_income_statements("AAPL", period_type="quarterly", limit=4)
balance = client.get_balance_sheets("AAPL", period_type="annual", limit=5)
cashflow = client.get_cash_flow_statements("AAPL", period_type="quarterly", limit=4)
# Fetch annual SEC filings for AI analysis
filings = client.get_sec_filings("AAPL", form_type="10-K", limit=20)

Frequently Asked Questions

Quick answers about dataset quality, MCP usage, SDK support, and production readiness.

Ready to Build Something Amazing?

Join teams building finance products, AI analyst tools, and internal workflows on top of apifinance.ai.

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