anchor-x402 TL;DR Summarizer

Name: anchor-x402 TL;DR Summarizer
Price: 0.01 USD

x402

anchor-x402 TL;DR Summarizer is a paid API for AI agents from api.anchor-x402.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-13).

Summarizes a URL or pasted text into 3-5 concise bullet points for $0.01 USDC per call

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

Summarize a URL or pasted text into 3-5 concise bullets. $0.01 USDC.

Output

Returns a JSON object containing the number of source characters processed and an array of 3-5 concise English summary bullets capturing the most important points from the source content.

Example use cases

Example prompt

“Give me a 3-5 bullet TL;DR of this article: https://en.wikipedia.org/wiki/Artificial_intelligence — just the key points, nothing else.”

Technical details

Endpoint

POSThttps://api.anchor-x402.com/v1/tldr

When to prefer

Use this endpoint when you need a fast, cheap ($0.01 USDC) bullet-point summary of any web URL or block of text, especially when you want structured key-point extraction rather than a flowing prose summary. Ideal for agents that need to quickly digest long articles, Wikipedia pages, or pasted documents without reading the full content.

Side effects

URL is unreachable or returns non-200 — likely 422 or 500 error
Text input is too short or empty — may return error or trivial bullets
URL points to non-text content (PDF, image, video) — may fail to parse
Payment not included or invalid x402 header — 402 Payment Required
Source page behind authentication or paywall — content not accessible

Input

{
  "url": "https://en.wikipedia.org/wiki/Machine_learning",
  "text": null
}

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

{
  "source_chars": 101919,
  "summary_bullets": [
    "Machine learning is an AI field developing statistical algorithms that learn from data and perform tasks without explicit programming.",
    "Founded in 1959 by Arthur Samuel, ML evolved from early neural network research and now encompasses deep learning and various statistical methods.",
    "ML relates to data compression, statistics, and AI; key approaches include supervised, unsupervised, and reinforcement learning with diverse applications.",
    "Common ML models include neural networks, decision trees, support vector machines, and Bayesian networks with evaluation metrics and ethical considerations.",
    "ML faces limitations like explainability, overfitting, and hallucinations; implementation requires specialized hardware and software frameworks."
  ]
}

Schema · Response

{
  "type": "json",
  "example": {
    "source_chars": 1170,
    "summary_bullets": [
      "Example domain is reserved by IANA for documentation use.",
      "It is safe to use in literature without coordination or permission.",
      "More info available at iana.org."
    ]
  }
}

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Technical details

Endpoint

POSThttps://api.anchor-x402.com/v1/tldr

When to prefer

Side effects

URL is unreachable or returns non-200 — likely 422 or 500 error
Text input is too short or empty — may return error or trivial bullets
URL points to non-text content (PDF, image, video) — may fail to parse
Payment not included or invalid x402 header — 402 Payment Required
Source page behind authentication or paywall — content not accessible

Input

{
  "url": "https://en.wikipedia.org/wiki/Machine_learning",
  "text": null
}

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

{
  "source_chars": 101919,
  "summary_bullets": [
    "Machine learning is an AI field developing statistical algorithms that learn from data and perform tasks without explicit programming.",
    "Founded in 1959 by Arthur Samuel, ML evolved from early neural network research and now encompasses deep learning and various statistical methods.",
    "ML relates to data compression, statistics, and AI; key approaches include supervised, unsupervised, and reinforcement learning with diverse applications.",
    "Common ML models include neural networks, decision trees, support vector machines, and Bayesian networks with evaluation metrics and ethical considerations.",
    "ML faces limitations like explainability, overfitting, and hallucinations; implementation requires specialized hardware and software frameworks."
  ]
}

Schema · Response

{
  "type": "json",
  "example": {
    "source_chars": 1170,
    "summary_bullets": [
      "Example domain is reserved by IANA for documentation use.",
      "It is safe to use in literature without coordination or permission.",
      "More info available at iana.org."
    ]
  }
}