← The evidence ledger
Evidence row 47216
Observed , by sync.x402_bazaar/1.
- Kind
- listing
- About
- source_record x402_bazaar:https://agentsvc.io/api/v1/proxy/pdf-extract
- Recorded
- Previous row
- None: the first of its kind about this subject
- Body SHA-256
- ebec1936e8b442e37b6b1569b941d31024943575e5e283237c6e1a60c3b50532
- Leaf
- 0x191052a31a2c00e03f6db32db9a58d528b73b05196fddae3dfb4bac738f32dc2
What was observed
{
"endpoints": [
[
"x402",
"https://agentsvc.io/api/v1/proxy/pdf-extract"
]
],
"name": "PDF Text Extraction",
"owner_address": "0x622efa6ceb83a5b6edbc931332f4db68b7f74f27",
"raw": {
"accepts": [
{
"amount": "5500",
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"extra": {
"name": "USD Coin",
"version": "2"
},
"maxTimeoutSeconds": 300,
"network": "eip155:8453",
"payTo": "0x622efA6Ceb83a5b6EdBC931332f4db68B7f74F27",
"scheme": "exact"
},
{
"amount": "5500",
"asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
"extra": {
"feePayer": "Hc3sdEAsCGQcpgfivywog9uwtk8gUBUZgsxdME1EJy88"
},
"maxTimeoutSeconds": 300,
"network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
"payTo": "5JzPS5qpEwUM1vTC9GG7uqvwJdHzbEwfoPHf2Ki977tg",
"scheme": "exact"
},
{
"amount": "5500",
"asset": "0x3c499c542cEF5E3811e1192ce70d8cC03d5c3359",
"extra": {
"name": "USD Coin",
"version": "2"
},
"maxTimeoutSeconds": 300,
"network": "eip155:137",
"payTo": "0x622efA6Ceb83a5b6EdBC931332f4db68B7f74F27",
"scheme": "exact"
},
{
"amount": "5500",
"asset": "0xaf88d065e77c8cC2239327C5EDb3A432268e5831",
"extra": {
"name": "USD Coin",
"version": "2"
},
"maxTimeoutSeconds": 300,
"network": "eip155:42161",
"payTo": "0x622efA6Ceb83a5b6EdBC931332f4db68B7f74F27",
"scheme": "exact"
}
],
"description": "Extract all text from a PDF. Send as pdf_base64 (base64-encoded PDF, max ~10 MB decoded). Returns text (full concatenated text), pages array (per-page text + char_count), page_count, and metadata (title, author, creator). Encode with: Buffer.from(pdfBytes).toString('base64'). Ideal for RAG pipelines, document QA, or LLM ingestion.",
"extensions": {
"bazaar": {
"category": "text",
"info": {
"input": {
"body": {
"max_pages": 10,
"pdf_base64": "<base64 of a PDF>"
},
"bodyType": "json",
"method": "POST",
"type": "http"
},
"output": {
"example": {
"data": {
"extracted_at": "2026-04-10T14:00:00.000Z",
"file_size_bytes": 245760,
"metadata": {
"author": "Finance Department",
"creator": "Adobe Acrobat",
"title": "Q1 2026 Financial Report"
},
"page_count": 12,
"pages": [
{
"char_count": 54,
"page": 1,
"text": "Quarterly Financial Report Q1 2026\n\nExecutive Summary"
},
{
"char_count": 58,
"page": 2,
"text": "Table of Contents\n1. Revenue Overview\n2. Cost Analysis"
}
],
"text": "Quarterly Financial Report Q1 2026\n\nExecutive Summary\nTotal revenue increased by 23% year-over-year..."
},
"success": true
},
"type": "json"
}
},
"schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"body": {
"properties": {
"max_pages": {
"default": 50,
"description": "Maximum number of pages to extract. Default: 50. Use to limit processing time for large PDFs.",
"type": "integer"
},
"pdf_base64": {
"description": "Base64-encoded PDF file content. Decode a PDF file to base64 and pass it here. Max ~10 MB (unencoded).",
"type": "string"
}
},
"required": [
"pdf_base64"
]
},
"bodyType": {
"enum": [
"json",
"form-data",
"text"
],
"type": "string"
},
"method": {
"enum": [
"POST",
"PUT",
"PATCH"
],
"type": "string"
},
"type": {
"const": "http",
"type": "string"
}
},
"required": [
"type",
"method",
"bodyType",
"body"
],
"type": "object"
},
"output": {
"properties": {
"example": {
"properties": {
"data": {
"properties": {
"extracted_at": {
"description": "ISO 8601 timestamp of extraction",
"format": "date-time",
"type": "string"
},
"file_size_bytes": {
"description": "Size of the decoded PDF in bytes",
"type": "integer"
},
"metadata": {
"description": "PDF document metadata (if available)",
"properties": {
"author": {
"type": "string"
},
"creation_date": {
"type": "string"
},
"creator": {
"type": "string"
},
"producer": {
"type": "string"
},
"subject": {
"type": "string"
},
"title": {
"type": "string"
}
},
"type": "object"
},
"page_count": {
"description": "Total number of pages in the PDF",
"type": "integer"
},
"pages": {
"description": "Per-page text content (first max_pages pages)",
"items": {
"properties": {
"char_count": {
"description": "Number of characters on this page",
"type": "integer"
},
"page": {
"description": "Page number (1-based)",
"type": "integer"
},
"text": {
"description": "Extracted text for this page",
"type": "string"
}
},
"type": "object"
},
"type": "array"
},
"text": {
"description": "Full extracted text from all pages, joined with newlines. Preserves paragraph structure where possible.",
"type": "string"
}
},
"required": [
"text",
"page_count",
"extracted_at"
],
"type": "object"
},
"success": {
"type": "boolean"
}
},
"required": [
"success",
"data"
],
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
},
"tags": [
"pdf-to-text",
"pdf-parsing",
"pdf",
"text",
"extract",
"parse",
"document",
"ocr",
"rag",
"llm"
]
},
"builder-code": {
"info": {
"a": "bc_q6wi3opd"
}
}
},
"iconUrl": "https://res.cloudinary.com/bdb-prod/image/upload/c_fill,ar_1:1,w_256,h_256,f_auto,q_auto/v1791462963/bazaar-icons/243ff0b79dbee242.png",
"lastUpdated": "2026-10-26T04:11:47.899Z",
"quality": {
"l30DaysTotalCalls": 10,
"l30DaysUniquePayers": 1,
"lastCalledAt": "2026-10-26T04:11:47.689Z"
},
"resource": "https://agentsvc.io/api/v1/proxy/pdf-extract",
"serviceName": "PDF Text Extraction",
"tags": [
"pdf-to-text",
"pdf-parsing",
"pdf",
"text",
"extract"
],
"type": "http",
"x402Version": 2
},
"source": "x402_bazaar",
"summary": "Extract all text from a PDF. Send as pdf_base64 (base64-encoded PDF, max ~10 MB decoded). Returns text (full concatenated text), pages array (per-page text + char_count), page_count, and metadata (title, author, creator). Encode with: Buffer.from(pdfBytes).toString('base64'). Ideal for RAG pipelines, document QA, or LLM ingestion."
}What the leaf hashes
{"body":"ebec1936e8b442e37b6b1569b941d31024943575e5e283237c6e1a60c3b50532","id":47216,"kind":"listing","method":"sync.x402_bazaar/1","observed_us":1793025409231541,"prev":null,"subject":"x402_bazaar:https://agentsvc.io/api/v1/proxy/pdf-extract","subject_kind":"source_record"}Proof
Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of (296695 rows).
- 0x19104d45053aa58387807f8a31ccaed632503a8e1332ec6d94eadea84763c11a
- 0x94cd81bb4fa18b87f621f38f964228603004543db9bdfcdfa86481c130607d66
- 0x856ef3f181bf2eb9108a5698036e332ac8100ab062f32d9cf7137ddc37ace3ba
- 0x37bf9adca4791aa00024373ee443eca6ecb7b63700c51a6a583f6a0c64495759
- 0x1488168e35ab5c39e55aa74df6a22bd0d09475a4cbeceb0399ab0b57c8d3d904
- 0xcfce1d169f179ed8bff1d9e50df093f42e1cd265fb91e5e743604a97e1766789
- 0x94d2e3c569f495f77a3f85acb20e3d910925b81ed3b2073cf83a74d35fb9d59e
- 0xed885f377c16339db2936d039f2acf8720060c23d4a17be3cdaabe4c98a88ce9
- 0x50566d6198ca8c3af7617938ce8dd24b33dd4d078ae3044e0096ee9b02583a12
- 0x22a40154924d75fee76be762401605048bce3556b7a3714fb3a594b3875c3a06
- 0x0ebd017eb4cb5f6f3c01ae6562748898aa3fb42b150504be21c4050acea38366
- 0x6a8a72430804a14ec575375f3d0ca4364f58310afb2c2e8c92b72f8b75809db9
- 0xe00c7829e46541e49a308807d357db902e06eff941a833217a8f0dbccd3964f1
- 0x770e4376196188c7fc9ec40f8e218dc0d1cb92bba5fe0425a0edce27ca9a997f
- 0x60582ab4b41e72eb70eb0bce07fbf0fb4d2b62a8bad5be50f5d16d511adc746c
- 0xc6a4d7899a0f442958e26505011190b7cff2483611c1b94eba9043e0ba9ac36e
- 0x8c756542e482157df0c36464d75c937b845e4423dbbcfad08f0cf8d5a16b9a13
- 0x20704208aa6923d6b2450ef74ede87a6e0265d46447926038fe9506e9e154b2d
- 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a
- Day root
- 0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8
- Chained
- 0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a
- Published
- On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae