LinkedIn article, ready to publish (research + copy + sized images)

Hand over a topic and get a publish-ready LinkedIn article back: grounded web research with cited sources, three headline options, a hook-first body under subheads with facts linked to their sources, key takeaways, a companion post with hashtags, and generated images delivered at LinkedIn's own sizes (article cover 1920x1080, link-share 1200x627, feed square and portrait, in-article 16:9) as inline JPEG under 3 MB. Send POST /v1/linkedin-article with the required field topic and pay $1.10 per call over x402 with USDC on an EVM chain. It returns a JSON object with report, title, article, post, images and 3 more.

Optional byline, angle, audience, tone and CTA. Nothing from memory; nothing posted on your behalf. One payment, one package. Not cached. Takes up to 3 minutes to answer: keep the connection open for at least 210 seconds, because a client that disconnects first loses the answer, or send the paid call with "Prefer: respond-async" to get a job link at once and collect the answer from it (payment still settles only on a delivered result).

Category: LLM gateway · Tags: linkedin linkedin-article ghostwriting thought-leadership social-media marketing images report

TRY IN PLAYGROUND →

Parameters

Example request

curl -i -X POST https://agent402.tools/v1/linkedin-article \
  -H "Content-Type: application/json" \
  -d '{"topic":"Why AI agents will pay for APIs with stablecoins","audience":"fintech product leaders","length":"standard"}'

Without payment this returns HTTP 402 Payment Required with the exact price for linkedin-article; any x402 v2 or MPP client pays it and retries.

Example response

{
  "report": "# Why agent payments settle on chain\n*One line of context*\n\n...\n\n## Sources\n1. [Example](https://example.com/...)\n\n---\n## Companion post (paste as a LinkedIn post, 1,120 of 3000 characters)\n...",
  "title": "Why agent payments settle on chain",
  "article": {
    "headline": "Why agent payments settle on chain",
    "headlines": [
      "Why agent payments settle on chain",
      "...",
      "..."
    ],
    "subtitle": "One line of context",
    "body_markdown": "...",
    "key_takeaways": [
      "..."
    ],
    "word_count": 980
  },
  "post": {
    "caption": "...\n#AgenticFinance #Payments #AI #Stablecoins",
    "hashtags": [
      "#AgenticFinance",
      "#Payments",
      "#AI",
      "#Stablecoins"
    ],
    "chars": 1120
  },
  "images": [
    {
      "slot": "cover",
      "alt": "An abstract network of nodes exchanging tokens",
      "prompt": "...",
      "source": {
        "width": 1024,
        "height": 1024,
        "model": "black-forest-labs/flux.2-klein-4b"
      },
      "files": [
        {
          "name": "article-cover",
          "use": "LinkedIn article cover image",
          "width": 1920,
          "height": 1080,
          "format": "jpeg",
          "media_type": "image/jpeg",
          "bytes": 412000,
          "b64": "/9j/4AAQ…"
        },
        {
          "name": "post-link-share",
          "use": "LinkedIn post / link-share image (1.91:1)",
          "width": 1200,
          "height": 627,
          "format": "jpeg",
          "media_type": "image/jpeg",
          "bytes": 180000,
          "b64": "/9j/4AAQ…"
        }
      ]
    }
  ],
  "sources": [
    {
      "n": 1,
      "title": "Example",
      "url": "https://example.com/...",
      "snippet": "...",
      "fullText": true
    }
  ],
  "tables": [],
  "meta": {
    "tier": "linkedin-article",
    "words": 980,
    "images": 2,
    "image_files": 5,
    "post_chars": 1120,
    "sources_listed": 12,
    "sources_full_text": 4,
    "synthesis_model": "anthropic/claude-opus-5"
  }
}

From an MCP client

catalog.call {
  "slug": "linkedin-article",
  "params": {
    "topic": "Why AI agents will pay for APIs with stablecoins",
    "audience": "fintech product leaders",
    "length": "standard"
  }
}

The hosted connector at https://agent402.tools/mcp needs a payment for linkedin-article; the stdio package pays it from a wallet or from AGENT402_CREDITS_KEY. Local install: npx -y agent402-mcp.

Errors and behavior

• topic is required. An input the tool rejects returns an HTTP 4xx whose body carries error, tool, expected, required and example, so the caller can correct it.
• A paid call that ends in any status of 400 or above is not charged over x402, MPP or a prepaid credits key: settlement is cancelled when the tool fails.
• Wallet-only: this tool runs a model, so it has no proof-of-work tier. A prepaid card-credits key issued earlier (Authorization: Bearer a402_...) also pays it.
• Model-backed: the answer is generated by a model, so the same input can produce different wording.
• Long-running: payment settles after the work finishes, so only EVM exact payments are offered.
• Takes up to 180 seconds. Keep the connection open that long, or send the paid call with Prefer: respond-async to get 202 and a job link at once, then collect the answer from GET /api/jobs/{id}; payment still settles only when the answer is ready.
• A GET or HEAD to /v1/linkedin-article returns the same 402 quote, so the price can be read without a body.
• An Idempotency-Key header makes a retried paid call replay the first 200 instead of charging again (an answer larger than 1 MB and a streamed response are not replayed).

Paid call (JavaScript agent)

import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client();
client.setSpendControls?.(false); // keep your own spending ceiling in code
registerExactEvmScheme(client, { signer: privateKeyToAccount(KEY) });
const payFetch = wrapFetchWithPayment(fetch, client);

const res = await payFetch("https://agent402.tools/v1/linkedin-article", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    "topic": "Why AI agents will pay for APIs with stablecoins",
    "audience": "fintech product leaders",
    "length": "standard"
  }),
});

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