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Agent Rescue Desk — PDF Citation Pack
x402
Agent Rescue Desk — PDF Citation Pack is a paid API for AI agents from agent-rescue-desk.netlify.app, paid per call via x402, $0.25/call, status unknown (last checked 2026-09-14).
Extracts structured, SHA-256-addressable citation blocks from text-layer PDFs so AI agents can cite sources with verifiable evidence addresses
Price / call$0.25
Success—
StatusUnknown
How this service works
Fixed-scope diagnosis and repair for one broken AI agent workflow or MCP integration. $1,000. Decisive outcome in 12 hours.
Output
A JSON response containing per-document citation blocks, each with a unique block ID (e.g. p1-b0001), page number, extracted text, bounding box coordinates, and a SHA-256 hash of the document. Also includes a markdown summary per document, page count, character count, processing time, quality warnings, and a nextStep instruction for using the SHA-256 plus block ID as a verifiable evidence address when citing.
Example use cases
Research paper citation for RAG pipeline
“I have a PDF of a scientific paper I want my agent to cite accurately — can you extract all the text blocks with page and block IDs plus a SHA-256 hash so I can use them as verifiable evidence addresses? Here's the base64-encoded PDF with id 'climate-study-2024'.”
Legal document evidence extraction
“Extract the text from this contract PDF into structured citation blocks with SHA-256 verification so my agent can reference specific clauses by page and block ID. The document id is 'contract-nda-v2' and here's the base64 content.”
Batch academic source indexing
“I have three academic PDFs I need my agent to be able to cite — please extract all the text blocks with bounding coordinates and SHA-256 hashes for each so I can build an evidence index. The ids are 'source-a', 'source-b', and 'source-c', and here are their base64 encodings.”
Technical details
Endpoint
POSThttps://agent-rescue-desk.netlify.app/api/agent402/pdf-citation-packWhen to prefer
Choose this endpoint when an AI agent needs to extract text from text-layer PDFs and produce verifiable, citable output with SHA-256-addressable block IDs. It is ideal for RAG pipelines, legal or academic document workflows, and any agent task requiring evidence traceability. It is not suitable for scanned/image PDFs (no OCR), redaction, accessibility certification, or layout-fidelity work. Prefer this over generic PDF parsers when citation integrity and agent-usable block IDs are the priority.
Side effects
- Batch exceeds 2,000,000 decoded bytes — request rejected
- More than 40 pages submitted across all documents — request rejected
- PDF is image-only (no text layer) — this is not OCR, extraction will be empty or minimal
- Unusual filter chains or high-expansion streams rejected by budget limits
- Oversized string tokens rejected
- Submission of passwords, regulated data, payment-card data, health data, or private keys — rejected by policy
- Invalid or malformed base64 encoding — parse error
- minLength of 40 characters not met in query field — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | A JSON string containing 1-5 authorized, non-sensitive text-layer PDFs as raw padded base64. Each document needs a unique URL-safe id. The decoded batch must be at most 2,000,000 bytes and 40 pages. Server-parsed streams need direct numeric lengths and must remain within the published filter, expansion, and token budgets. Do not submit passwords, regulated data, payment-card data, health data, private keys, or unrelated customer records. |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"batch": {
"pages": 1,
"documents": 1,
"characters": 19,
"decodedBytes": 594,
"citationBlocks": 1,
"processingMilliseconds": 12
},
"limits": "Bounded text-layer extraction with direct numeric stream lengths and strict filter, expansion, and string-token budgets. Unusual filter chains, high-expansion streams, and oversized string tokens are rejected. This is not OCR, redaction, legal review, accessibility certification, or layout-fidelity certification.",
"status": "complete",
"privacy": "Submitted PDF bytes were processed transiently and were not stored or logged by the product. The response intentionally contains the requested extracted source text.",
"service": "Agent Rescue Desk Agent-Citable PDF Extraction Pack",
"version": "2026-07-28",
"nextStep": {
"instruction": "Use each SHA-256 plus page/block id as the evidence address when an agent cites the extracted text.",
"privateKitUrl": "https://agent402.app/agents/6c54e490-72fc-47ba-bad1-3ac47b1b464a/private-pdf-citation-kit/invoke",
"privateKitPriceUsd": 39
},
"documents": [
{
"id": "synthetic-example",
"blocks": [
{
"id": "p1-b0001",
"page": 1,
"text": "Hello citation pack",
"bounds": [
0.117647,
0.075758,
0.159098,
0.015152
]
}
],
"sha256": "69860fd72f0e874f332d71e832088707cee3c8705860b73d12681293e0b11f2d",
"markdown": "# synthetic\\-example\n\n- SHA\\-256: `69860fd72f0e874f332d71e832088707cee3c8705860b73d12681293e0b11f2d`\n- Pages: 1\n- Extracted characters: 19\n\n## Page 1\n\n- [p1-b0001] Hello citation pack",
"metadata": {
"PDFFormatVersion": "1.4"
},
"pageCount": 1,
"characters": 19,
"qualityWarnings": []
}
]
}
}Tags
x402
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