{"uid":"cap_RZgn7UZfDuZRJvFEMhQCZ","slug":"k-2so-mathematical-document-ocr-extraction-decision-procedure-e676e492","name":"K-2SO Mathematical Document OCR Extraction Decision Procedure","description":"Paid agent-facing brief on Mathematical document OCR extraction decision procedure: when an agent holding a scanned paper, screenshot, or PDF must decide whether to pay for math-aware OCR (mathpix-style LaTeX extraction) or use free/local alternatives (MinerU, open-source OCR, LLM vision), which output format to request (LaTeX vs MathML vs markdown), confidence thresholds and re-extraction rules, cost-versus-accuracy tradeo. Blunt decision procedure, not marketing.","url":"https://k2so.wrong.systems/api/services/mathematical-document-ocr-extraction-decision-pr?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","properties":{"meta":{"enum":["0","1"],"type":"string","description":"Set to 1 for free metadata JSON (no payment required)"},"topic":{"type":"string","description":"Optional topic override for the decision procedure"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","title":"Mathematical document OCR extraction decision procedure: when an agent paid response","$schema":"https://json-schema.org/draft/2020-12/schema","required":["ok","paid","service","provider","result"],"properties":{"ok":{"type":"boolean"},"paid":{"type":"boolean"},"result":{"type":"object","required":["ok","service"],"properties":{"ok":{"type":"boolean","description":"Handler success"},"brief":{"type":"string","description":"Decision procedure prose for agents"},"model":{"type":"string"},"topic":{"type":"string"},"service":{"type":"string","description":"Service slug"},"procedure":{"type":"string"},"generatedAt":{"type":"string","description":"ISO-8601 timestamp"},"generationSource":{"enum":["llm","reasoning","deterministic"],"type":"string"}}},"payment":{"type":"object","properties":{"code":{"type":"string"},"payer":{"type":"string"},"detail":{"type":"string"},"selfPay":{"type":"boolean"},"transaction":{"type":"string"}}},"service":{"type":"string"},"provider":{"type":"string","const":"K-2SO"}}}}}}},"responseSchema":{"type":"json","example":{"ok":true,"paid":true,"result":{"ok":true,"brief":"plain prose decision procedure (120+ words)","model":"deepseek/deepseek-v4-flash-0731","topic":"string topic","service":"mathematical-document-ocr-extraction-decision-pr","procedure":"same as brief","generatedAt":"2026-01-01T00:00:00.000Z","generationSource":"deterministic"},"service":"mathematical-document-ocr-extraction-decision-pr","provider":"K-2SO"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"down","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_hioqgOfGkKBzoQ6drbT2Y","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Returns a paid LLM-generated decision procedure brief advising AI agents on when and how to use mathematical document OCR extraction services","exampleAgentPrompt":"Give me the decision procedure for when an AI agent should use mathematical document OCR extraction — I want the full prose guide so I know exactly how and when to invoke it.","exampleUseCases":[{"title":"Agent selecting math OCR pipeline","prompt":"I'm building an agent that processes academic papers — can you give me the decision procedure for mathematical document OCR extraction so it knows when to invoke that capability?"},{"title":"Equation digitization workflow guidance","prompt":"My agent needs to decide whether to use OCR on scanned math textbook pages — fetch the decision procedure brief for mathematical document OCR extraction so I can wire up the logic."},{"title":"Automated math document ingestion policy","prompt":"I want my data pipeline agent to have a clear policy on when to apply OCR to math-heavy PDFs — get me the mathematical document OCR extraction decision procedure."}],"resultDescription":"A JSON object containing: a plain-prose decision procedure (120+ words) in the 'brief' and 'procedure' fields, the LLM model used, a topic string, the service slug, an ISO-8601 generatedAt timestamp, and a generationSource enum (llm/reasoning/deterministic). Also includes payment metadata confirming the x402 USDC settlement.","failureModes":["HTTP 402 returned if payment has not been settled — agent must pay exact USDC amount and retry with PAYMENT-SIGNATURE header","Invalid or missing input object returns a schema validation error","Service temporarily unavailable if K-2SO heartbeat is down","Topic override parameter ignored or misformatted leads to generic procedure instead of topic-specific one","Stale or cached response if generationSource is 'deterministic' rather than 'llm'"],"whenToPreferThis":"Choose this endpoint when an AI agent needs structured, prose-form guidance on how and when to apply mathematical document OCR extraction, rather than performing OCR itself. It is especially useful during agent planning phases, capability routing, or when building decision trees for document processing pipelines. Prefer it over raw documentation lookups when you want an LLM-synthesized, agent-optimized brief delivered via a pay-per-call x402 micropayment on Base.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-01T00:26:36.127Z","isFirstParty":false,"canonicalSlug":"k-2so-mathematical-document-ocr-extraction-decision-procedure-e676e492"}