For AI agents and developers

Earnings briefs your agent can read

Every EarningsNxt brief is published as JSON and Markdown at the same canonical URL the humans use — with an llms.txt discovery doc and stable per-ticker paths. Plug it into ChatGPT, Claude, Perplexity, an MCP server, or your own LLM pipeline in minutes.

Get a Pro API key API reference Read llms.txt

Formats

Three machine-readable formats

Same data, three surfaces. Pick whichever matches your stack — or point a crawler at the HTML page and follow the <link rel="alternate"> tags we emit on every brief.

JSON

/api/v1/briefs/{symbol}/{quarter}

Structured response with company, estimates, results, and full pre/post brief content. Ideal for tool-use, RAG indexing, and agent workflows.

Markdown

/ticker/{symbol}/earnings/{quarter}.md

The same brief rendered as plain Markdown at the canonical page URL. Drop-in for LLM context, no HTML scraping required.

llms.txt

/llms.txt

Discovery document describing the API surface, tiers, rate limits, and brief schema — written for LLMs to ingest directly.

Quick start

One curl away

Free tier (top 10 tickers, no auth) works with a single curl. Pro tickers require a Bearer token, which you generate under API access on your account page. Every endpoint and parameter is in the API reference.

Get a brief as JSON

curl https://earningsnxt.ai/api/v1/briefs/AAPL/Q1-2026

Same brief as Markdown

curl https://earningsnxt.ai/ticker/AAPL/earnings/Q1-2026.md

Authenticated Pro request

curl -H "Authorization: Bearer $EARNINGSNXT_KEY" \
     https://earningsnxt.ai/api/v1/briefs/VZ/Q1-2026

Discover the API

curl https://earningsnxt.ai/llms.txt

Prompts

Drop into your prompt

The Markdown URLs are designed to be pasted directly into a model with browsing or used as raw context in an SDK call.

ChatGPT / Claude / Perplexity (with web access)

Read https://earningsnxt.ai/ticker/NVDA/earnings/Q1-2026.md
and summarize the bull and bear cases.

From an Anthropic SDK script

import anthropic, urllib.request

brief = urllib.request.urlopen(
    "https://earningsnxt.ai/ticker/NVDA/earnings/Q1-2026.md"
).read().decode()

client = anthropic.Anthropic()
client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": f"Summarize this earnings brief:\n\n{brief}",
    }],
)

Tiers

Tiers and rate limits

Free

Rate limit: 60 req/min
Coverage: Top 10 tickers

Full earnings calendar
Ticker directory & search
JSON, Markdown, llms.txt
No auth required

ProMost popular

$8/mo

Rate limit: 60 req/min · 2,000 req/day
Coverage: All tracked tickers

Every covered ticker, all quarters
Bearer-token API key
Email alerts & weekly digests
Historical brief archive

Reference

The details

Authentication

Pass your API key as a Bearer token in the Authorization header. Free-tier endpoints work without one. Rate limits and 401/429 responses are documented in the API reference.

Authorization: Bearer enx_...

Auto-discovery from any brief page

Every HTML brief page exposes its machine-readable variants via standard alternate-link tags, so a crawler that lands on the human URL can switch to JSON or Markdown without guessing.

<link rel="alternate" type="text/markdown"
      href="https://earningsnxt.ai/ticker/AAPL/earnings/Q1-2026.md">
<link rel="alternate" type="application/json"
      href="https://earningsnxt.ai/api/v1/briefs/AAPL/Q1-2026">

Use and attribution

Build agents, research tools, or downstream products on top of EarningsNxt briefs. We ask for attribution when the brief is shown to end users:

Source: EarningsNxt (https://earningsnxt.ai)

Briefs are AI-assisted analysis, not financial advice. Pass the full disclaimer through to your end users where appropriate.

Ready to wire it up?

Free tier needs no signup — start hitting the JSON and Markdown endpoints immediately. Sign up when you want a key for the full ticker universe.

Create a Pro key Try the JSON endpoint

Earnings briefs your agent can read

import anthropic, urllib.request brief = urllib.request.urlopen( "https://earningsnxt.ai/ticker/NVDA/earnings/Q1-2026.md" ).read().decode() client = anthropic.Anthropic() client.messages.create( model="claude-opus-4-7", max_tokens=1024, messages=[{ "role": "user", "content": f"Summarize this earnings brief:\n\n{brief}", }], )