The 10 Best Financial APIs for AI Coding Tools (2026)

Cursor can scaffold a fintech app in twenty minutes. Claude Code can wire the backend, write the tests, and ship a working build before lunch.

None of that means the app is correct.

If you're:

• building a portfolio tracker,
• vibe-coding a stock screener,
• or shipping a trading dashboard for a client,

the API you plug in decides whether the AI gets it right the first time, or quietly invents endpoints that don't exist.

Your AI coding tool is only as good as the docs it's reading

Here's what actually happens. You open Cursor, paste a financial API's docs link, and ask it to build a price-history endpoint. Half the time it works. The other half, it hallucinates a parameter that was deprecated two years ago, or guesses at a response shape that hasn't matched reality since the provider's last redesign.

That's not a model problem.

It's a documentation problem. Most financial data providers wrote their docs for a human skimming a browser tab, not for a model parsing structure. No llms.txt. No machine-readable OpenAPI spec. No SDK that matches what's actually in the docs.

Developers discover this the expensive way: after the AI ships broken code with confidence, and nobody catches it until the demo.

Coverage matters less than legibility.

In 2026, the financial APIs worth using with an AI coding tool aren't necessarily the ones with the most tickers. They're the ones whose documentation, specs, and SDKs are clean enough that Cursor, Claude Code, Windsurf, or Copilot can generate correct code against them on the first pass.

What to check before you pick one

Before wiring any financial API into an AI-assisted workflow, look for four things:

• Machine-readable docs — an llms.txt file, an OpenAPI 3.x spec, or both
• An official SDK in your stack's language, not just community wrappers
• A real free tier, so the AI can be tested against live responses while you prototype
• Predictable pricing, because AI coding tools tend to make you move fast, and fast means more API calls than you planned for

I use EODHD for most of this kind of work, mainly because its OpenAPI spec and AI Agent Skills are built for exactly this. 👉 Get an EODHD key here

Here are the ten that hold up.

1. EODHD — Best all-around for AI-assisted building

EODHD covers 60+ exchanges and 150,000+ tickers, with 30+ years of history on major markets, all returned as clean JSON.

What makes it the strongest pick for coding with AI specifically is the sheer number of on-ramps: an official MCP server with 75 tools, an OpenAPI 3.1 spec covering 74 endpoints, AI Agent Skills built for Claude Code and Codex-style agents, and a ChatGPT assistant trained on its own documentation. When you ask Cursor or Claude Code to build against it, the model has a machine-readable spec to read instead of guessing from prose.

Pros: Broadest AI on-ramp of any provider here, global coverage, fundamentals and technicals under one key, accessible pricing.

Cons: Real-time runs over per-ticker WebSocket rather than ultra-low-latency feeds. US options is a paid add-on.

Pricing: Free tier, then roughly €20–€100/month depending on real-time and intraday access; commercial plans from around €400/month.

Best for: Anyone building a fintech app end-to-end with an AI coding tool and wants one API instead of five.

👉 Grab a free EODHD key and point your AI coding tool at it

2. Massive (formerly Polygon.io) — Best for real-time, latency-sensitive apps

Polygon rebranded to Massive in 2026, though most developers still call it Polygon out of habit. The product underneath is unchanged: tick-by-tick trades, WebSocket streaming, and low-latency US equities and options data.

Its MCP server takes an unusual approach. Instead of one tool per endpoint, it gives the model three composable tools, search, call, and query, that cover the entire API surface and stay in sync automatically as Massive ships new endpoints. For an AI coding tool exploring an API it's never seen, that's a meaningfully shorter learning curve.

Pros: Real-time WebSockets, options with Greeks, a well-designed MCP that scales with the API instead of falling behind it.

Cons: US-centric. Real-time access sits on higher tiers.

Pricing: Free tier with delayed data; paid stock plans roughly $29–$199+/month, with real-time gated to higher tiers.

Best for: Trading dashboards and live-data apps where milliseconds matter.

3. Alpaca — Best for apps that need to act, not just read

Alpaca is a self-clearing broker-dealer, which means it's the rare entry on this list where the AI-built app doesn't just display data, it can place trades.

Alpaca has leaned hard into the coding-tool angle specifically. Its Trading MCP Server and a new Trading CLI are both built from its published OpenAPI specs and explicitly documented to work with Claude Code, Cursor, VS Code, Gemini CLI, and PyCharm. Account creation is free, there's no minimum deposit, and paper trading with $100K in simulated funds means an AI coding tool can build and test a full trading flow without touching real money.

Pros: Free brokerage account, paper trading out of the box, MCP and CLI built around AI coding workflows, equities/options/crypto in one API.

Cons: Full real-time market coverage (beyond the free IEX feed) requires the paid Algo Trader Plus tier. It's brokerage-first, not a general research dataset.

Pricing: Free account and Basic market data; paid tier for full real-time stock and options coverage.

Best for: Apps where the AI needs to read market data and execute on it, with a safe paper-trading sandbox while you build.

4. Financial Modeling Prep — Best for fundamentals-heavy apps

If your AI coding tool's job is reading balance sheets instead of chasing ticks, FMP is the deepest option here. It exposes income statements, ratios, DCF models, filings, transcripts, and institutional holdings across 100+ endpoints, in both REST and WebSocket form.

FMP's documentation is consistently cited as one of the easier financial APIs to scaffold against, partly because the endpoint structure is predictable across asset classes once an AI coding tool learns the pattern from one or two examples.

Pros: Deepest fundamentals and ratios, generous endpoint count, JSON and CSV both supported.

Cons: Real-time data, full global coverage, and earnings transcripts live behind the higher tiers.

Pricing: Free (250 calls/day), Starter around $19/month, Premium around $69/month, Ultimate around $139/month for global coverage and transcripts.

Best for: Valuation tools, equity-research dashboards, and any app where the AI is reasoning about a company's financials.

5. Alpha Vantage — Best for learning the workflow

Alpha Vantage shows up in nearly every roundup of AI-friendly financial APIs, and it earns the spot. It runs an official MCP server, covers 200,000+ tickers across 20+ exchanges, and ships a deep technical-indicator library so the AI doesn't have to compute RSI or MACD by hand.

It's also become the default data backbone behind several open-source multi-agent trading frameworks, which means there's a large body of public code an AI coding tool has effectively already seen during training. That translates into fewer hallucinated calls when you ask it to wire Alpha Vantage into a new project.

Pros: Official MCP, strong indicator library, huge base of public examples and documentation.

Cons: The free tier is heavily rate-limited and you'll hit the wall fast once you start building seriously.

Pricing: Free key with strict limits; premium plans from roughly $50/month.

Best for: Learning the AI-coding-tool-plus-financial-API workflow before committing to a paid provider.

6. Finnhub — Best free tier

Finnhub's free tier is genuinely usable, not a teaser. Sixty calls per minute covers prototyping comfortably, and it includes real-time US quotes, fundamentals, SEC filings, and news with sentiment scores across 60+ global exchanges.

Where it gets interesting for an AI-assisted app is the alternative data: insider sentiment, earnings-call transcripts, lobbying records, and ESG scores, the kind of signal that usually sits behind an expensive institutional feed. There's no single official MCP server, but the docs are clean enough that Cursor or Claude Code can generate a working wrapper from the OpenAPI reference in a few minutes.

Pros: Best free tier in this list, rich alternative data, global coverage.

Cons: No official MCP server, so you're either using a community one or writing your own thin wrapper.

Pricing: Free (60 calls/minute); premium tiers roughly $12–$100/month.

Best for: Side projects and sentiment-driven apps that need to start free and scale gradually.

7. Tiingo — Best lightweight option

Tiingo is the API to reach for when you don't need the firehose. It covers US equities, end-of-day and intraday pricing, fundamentals, crypto, forex, and financial news, with documentation simple enough that an AI coding tool rarely trips over it.

It also ships an MCP server with prompt templates for repeatable analysis tasks, which is a nice touch for a provider this size.

Pros: Clean, predictable docs, decent news coverage, inexpensive paid tiers.

Cons: Narrower than the all-rounders. No deep options or macro coverage, and real-time relies on IEX rather than the full US tape.

Pricing: Free tier for prototyping; low-cost paid plans for higher limits.

Best for: Lightweight US-equity apps and side projects where simplicity beats breadth.

8. Twelve Data — Best for multi-asset, indicator-driven apps

Twelve Data covers stocks, forex, crypto, ETFs, and indices from over 250 exchanges through one consistent API and WebSocket structure, with 100+ technical indicators built in.

The part that matters for AI-assisted coding is consistency. Every endpoint shares the same logic and the same response shape, and an OpenAPI/Swagger spec is published directly for generating client code. That uniformity is exactly what reduces the odds of an AI coding tool inventing a parameter that doesn't exist.

Pros: Clean multi-asset coverage, 100+ indicators, consistent API and WebSocket design, published OpenAPI spec.

Cons: Paid tiers can feel pricier than alternatives offering broader datasets at a similar cost. Fundamentals are thinner than an all-in-one provider.

Pricing: Free Basic plan for US stocks, forex, and crypto; paid plans starting around $29/month.

Best for: Dashboards that mix asset classes and lean on technical indicators.

9. Financial Datasets (financialdatasets.ai) — Best built-for-AI option

Most providers on this list retrofitted AI support onto an API built for humans. Financial Datasets did the opposite: it was designed from the start as a stock market API for AI agents and LLM-powered tools.

It covers 27,000+ tickers and 30+ years of history, including financial statements, equity prices, insider transactions, and full-text SEC filings an AI coding tool can pull directly into context. Five of the most common tickers (AAPL, GOOGL, MSFT, NVDA, TSLA) are free to query, which makes it unusually easy to prototype against before paying for full coverage.

Pros: Purpose-built for AI/LLM consumption, full-text SEC filing access, generous free sandbox on major tickers.

Cons: Younger product than the established players, so the ecosystem of examples and community SDKs is smaller.

Pricing: Free for five major tickers; paid plans for full 27,000+ ticker coverage.

Best for: Agent-style apps that need to reason over filings and statements, not just prices.

10. Intrinio — Best when you outgrow the others

Intrinio is the enterprise option here, and it's upfront about who it's for: fintechs and financial institutions that need licensed, audit-ready data and are willing to pay for it.

What's notable is how directly its marketing addresses AI-assisted building. Intrinio normalizes its data specifically so it plugs into Claude, ChatGPT, and custom models without a transformation layer, and it ships SDKs in Python, Ruby, and JS with sandbox environments for testing. The honest gap: there's no first-party MCP server yet, so an AI coding tool building an agent-style integration needs a custom wrapper rather than a drop-in connection.

Pros: Normalized, audit-ready data built for AI consumption, strong SDKs, broad asset-class coverage including options and ETFs.

Cons: No free production plan, only a limited sandbox. No first-party MCP server. Pricing scales fast once you're past prototyping.

Pricing: Free developer sandbox; production packages typically run from a few hundred to several thousand dollars per year depending on dataset.

Best for: Funded fintech teams that need licensed, compliance-ready data behind an AI-assisted product.

Quick comparison

How to pick yours

You're prototyping solo. Start with EODHD or Alpaca, both have real free tiers and AI on-ramps built specifically for coding tools, not retrofitted afterward.

You're building something fundamentals-heavy. FMP or EODHD. Both give an AI coding tool deep, structured financial statements to ground its output instead of inventing numbers.

You're building something that trades. Alpaca for the brokerage layer, Massive if you need lower-latency market data feeding into it.

Wiring one in: what "AI-coding-tool-friendly" actually looks like

The difference between a provider with good AI on-ramps and one without shows up the moment you ask Cursor or Claude Code to build something.

Prompt: "Build a Python function that pulls the last 30 days of daily closing prices for a ticker using EODHD's API and returns them as a list of (date, price) tuples."

Because EODHD publishes an OpenAPI spec, the model reads structure instead of guessing from prose, and the output looks like this on the first try:

import requests
from datetime import date, timedelta

def get_closing_prices(ticker: str, api_key: str, days: int = 30):
    end = date.today()
    start = end - timedelta(days=days)
    url = f"https://eodhd.com/api/eod/{ticker}.US"
    params = {
        "api_token": api_key,
        "from": start.isoformat(),
        "to": end.isoformat(),
        "fmt": "json",
    }
    response = requests.get(url, params=params)
    response.raise_for_status()
    return [(row["date"], row["close"]) for row in response.json()]

No invented parameters. No guessed response shape. That's the entire point of choosing an API with machine-readable docs before you start vibe-coding around it.

From here you can build:

• a portfolio tracker that refreshes on a schedule
• a screener that filters by price action across a watchlist
• a dashboard the AI extends every time you add a new data point

Key takeaways

• Coverage matters less than legibility. The API your AI coding tool can read correctly beats the one with more tickers.
• Look for llms.txt, an OpenAPI spec, and an official SDK before anything else.
• EODHD and Alpaca currently have the most coding-tool-native on-ramps (MCP, CLI, Agent Skills) of the ten covered here.

FAQs

❓ What's the best financial API for AI coding tools like Cursor or Claude Code in 2026?
✅ It depends on what you're building. EODHD has the broadest AI on-ramp for general fintech apps. Alpaca is strongest if the app needs to place trades, not just display data. FMP wins for fundamentals-heavy dashboards.

❓ Do I need an MCP server, or is a regular REST API enough?
✅ A REST API is enough if you're willing to let the AI read the OpenAPI spec and generate the wrapper itself, which works well with providers like FMP or Twelve Data. An MCP server removes that step entirely, which matters more as the app grows.

❓ Which financial API has the best free tier for prototyping with AI?
✅ Finnhub (60 calls/minute) and EODHD both offer free tiers usable for real prototyping, not just a teaser. Alpaca's free paper-trading account is the best option if the app needs to simulate trades.

❓ Can an AI-built app actually place trades, or only read data?
✅ Most of the APIs on this list are read-only. Alpaca is the exception: it's a real brokerage, so an AI-built app can place orders and manage positions through the same API it uses to read market data.

❓ Why does documentation quality matter more than data coverage for AI coding tools?
✅ An AI coding tool generates code from what it can parse. A provider with a clean OpenAPI spec or llms.txt gives the model structure to read instead of prose to guess from, which is the difference between correct code on the first try and a silent hallucination that breaks in production.

The bottom line

The model isn't the bottleneck anymore. The documentation is.

Pick the financial API whose docs your AI coding tool can actually read, and the difference shows up in every function it generates after that.

👉 Start building with the EODHD API here

Looking for technical content for your company? I can help — LinkedIn · kevinmenesesgonzalez@gmail.com