Best financial data API for AI agents — comparison
Most "best financial data API" guides were written for backend engineers wiring a charting dashboard. Agents have different constraints: typed schemas matter more than rate limits, batched fan-out matters more than per-call latency, and a missing OFAC field on a counterparty lookup is worse than a slightly stale quote.
This page compares the financial data APIs people commonly evaluate for LLM and agent workloads — Jintel, Finnhub, Financial Modeling Prep (FMP), Alpha Vantage, and Polygon — across the dimensions that actually matter when the caller is a model.
At a glance
| Capability | Jintel | Finnhub | FMP | Alpha Vantage | Polygon |
|---|---|---|---|---|---|
| GraphQL schema | ✅ Single endpoint, typed sub-graphs | ❌ REST | ❌ REST | ❌ REST | ❌ REST |
| MCP server (Streamable HTTP) | ✅ Hosted, 15 typed tools | ❌ | ❌ | ❌ | ❌ |
| x402 pay-per-query | ✅ USDC on Base, no signup | ❌ | ❌ | ❌ | ❌ |
| Batch entity enrichment | ✅ entitiesByTickers returns market + risk + filings + news in one round trip | Partial (per-domain endpoints) | Partial (per-domain endpoints) | Partial | Partial |
| OFAC sanctions screening | ✅ Built-in sanctionsScreen | ❌ | ❌ | ❌ | ❌ |
| SEC EDGAR fallback for historical financials | ✅ Auto-fallback when primary upstream is thin | ❌ | ❌ | ❌ | ❌ |
| Federal litigation (CourtListener) | ✅ Entity.litigation | ❌ | ❌ | ❌ | ❌ |
| Government contracts (USAspending) | ✅ Entity.governmentContracts | ❌ | ❌ | ❌ | ❌ |
| Clinical trials / FDA events | ✅ For pharma counterparties | ❌ | ❌ | ❌ | ❌ |
| Free tier | 500k credits/month | 60 calls/minute | 250 calls/day | 25 calls/day | 5 calls/minute |
| Auth model | Bearer + x402 | API key | API key | API key | API key |
| Wallet-only auth (no signup) | ✅ SIWX + x402 | ❌ | ❌ | ❌ | ❌ |
| Free schema introspection | ✅ GraphiQL | ❌ | ❌ | ❌ | ❌ |
llms.txt + agent.txt | ✅ | ❌ | ❌ | ❌ | ❌ |
.well-known/agent-card.json | ✅ | ❌ | ❌ | ❌ | ❌ |
Why this comparison and not "data quality"? Quote latency, fundamentals coverage, and price-history depth are roughly comparable across the major vendors above. The differentiators that move the needle for agents are how callable the API is, not whether last-trade quote is 200ms or 2s stale.
When to choose Jintel
- You're building an agent that needs more than market data. Sanctions, federal litigation, government contracts, clinical trials, FDA events, supply-chain relationships, and SEC filings are sub-graphs on the same
Entity— one query covers them all. - You want typed tools, not REST glue. The MCP server at
https://api.jintel.ai/mcpexposes 15 typed tools that Claude Desktop, Cursor, Continue, and any MCP-compatible runtime can call natively. The same surface is alsoPOST /tools/<name>for runtimes that prefer plain HTTP. - The caller is the wallet. With x402 pay-per-query, autonomous agents call the API without a signup flow — the wallet signature on the payment proof is the identity. Pricing is per-query (AST-driven), not per-month, so an agent that runs once a quarter doesn't pay for capacity it doesn't use.
- You need to fan out across a watchlist.
entitiesByTickers(["AAPL", "MSFT", "NVDA", "GOOG", "TSLA"])returns five entities each with market + risk + regulatory + news + technicals in one round trip. Mercurius loaders deduplicate upstream calls within a request, so the upstream cost is one batched call per data source — not five. - You're evaluating compliance APIs and don't want a dedicated KYC vendor for the screening step. OFAC SDN screening is a public-data convenience built into the same schema. (For compliance-grade adjudication, pair with a registered KYC provider; Jintel is not a substitute.)
When to choose something else
- You need exchange-level real-time tick data. Jintel returns last-trade quotes; Polygon and Finnhub publish full L1/L2 streams. If you're building a trading system that prices off the order book, use a vendor that licenses direct exchange feeds.
- You only need a single domain (e.g. just fundamentals or just options). Single-domain APIs may be cheaper if you're not exercising the multi-domain fan-out.
- Your stack is REST-first and you have no plans to add an LLM. GraphQL's introspection and typed nesting are most valuable when an agent is the caller. For pure backend ETL that already knows the schema, REST works fine.
Migration notes
| If you're using… | The migration looks like… |
|---|---|
| Finnhub | Replace per-symbol quote + company-news + stock-symbol calls with one entitiesByTickers query. The Jintel schema covers Finnhub's public news + sentiment + earnings via Entity.news, Entity.sentiment, and Entity.earnings. |
| FMP | Replace per-symbol profile + income-statement + balance-sheet-statement + cash-flow-statement calls with one Entity.financials { income balanceSheet cashFlow } sub-graph. Historical depth is backstopped by SEC EDGAR XBRL companyfacts (free, 10+ years for US filers). |
| Alpha Vantage | Replace TIME_SERIES_DAILY_ADJUSTED with Entity.market.history. Replace RSI / MACD / BBANDS with Entity.technicals { rsi macd bb }. Replace OVERVIEW with entityByTicker. |
| Polygon | Replace per-symbol REST aggregates with priceHistory(tickers) — batched OHLCV. Real-time tick streams have no Jintel equivalent today. |
Try it
# Free schema introspection — no auth.
curl -s https://api.jintel.ai/graphiql
# One query covering five tickers' market data, news, and technicals.
curl -s https://api.jintel.ai/api/graphql \
-H 'authorization: Bearer jk_live_…' \
-H 'content-type: application/json' \
-d '{ "query": "{ entitiesByTickers(tickers: [\"AAPL\",\"MSFT\",\"NVDA\",\"GOOG\",\"TSLA\"]) { ticker market { quote { price } } news(filter: { limit: 3 }) { title date } technicals { rsi macd { histogram } } } }" }'
Or wire up the MCP server in Claude Desktop / Cursor / Continue — see Install Jintel in your AI tools.
Related
- Financial data API for AI agents — the deeper "why" behind the schema design
- Market research with AI agents — bull / bear case generation walkthrough
- Due diligence on counterparties — KYC-style counterparty research using sub-graphs
- Sanctions screening for AI agents — OFAC integration
- Portfolio copilot — building an LLM portfolio assistant
Authorship
This comparison is maintained by Jintel Engineering at YojinHQ. We update it as upstream vendors change their pricing or capability sets — last reviewed 2026-04-30. Source: https://github.com/YojinHQ/Jintel/blob/main/docs-site/docs/use-cases/best-financial-data-api-for-ai-agents.mdx — open an issue or PR if you spot a stale claim.