Data x402 offerings

Create an email validation job from a JSON list of addresses or CSV text (send exactly one). Poll its status, then fetch results as JSON or CSV. Billing counts only testable addresses, fixed at creat…

Create an email validation job from a JSON list of addresses or CSV text (send exactly one). Poll its status, then fetch results as JSON or CSV. Billing counts only testable addresses, fixed at creat…

Create an email validation job from a JSON list of addresses or CSV text (send exactly one). Poll its status, then fetch results as JSON or CSV. Billing counts only testable addresses, fixed at creat…

Create an email validation job from a JSON list of addresses or CSV text (send exactly one). Poll its status, then fetch results as JSON or CSV. Billing counts only testable addresses, fixed at creat…

Create an email validation job from a JSON list of addresses or CSV text (send exactly one). Poll its status, then fetch results as JSON or CSV. Billing counts only testable addresses, fixed at creat…

Return a compact agent-readiness score for OpenAPI or MCP input.

Parse iCalendar (.ics) content — meeting invites, conference calendars, exports from Google/Outlook — into structured event JSON: title, start/end, location, organizer and description. Use this when an agent needs to turn .ics calendar files into clean event JSON.

Fetch a PDF (by URL, SSRF-guarded) or accept it as base64 and extract all readable text with page count. Deterministic parsing — no language model, so nothing is invented. Text-based PDFs only; scanned images need OCR (not included). Use this when an agent needs to extract readable text from any PDF — agents can't read PDFs.

Extract every HTML table from a page (or raw HTML) as structured JSON: headers and rows kept intact. The deterministic answer to 'get the pricing table / comparison table / stats table from this page' — no vision model needed. Use this when an agent needs to hTML tables → structured JSON rows and columns.

Scan unstructured text and extract structured entities: email addresses, URLs, IPv4 addresses, phone numbers and domains. Deduplicated, categorized, machine-ready. Use this when an agent needs to pull emails, URLs, IPs, phones and domains from raw text.

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 (ti…

PDF text extraction for RAG: extract text from a PDF URL into RAG-ready, page-aware JSON. PDF text extraction / PDF to text — a public digital PDF becomes per-page text with page numbers (pages[].page, pages[].text), page count, a list of empty pages and the combined text, so RAG chunks can cite their page (no OCR). $0.01 USDC Base or Algorand. Caps 20 pages / 5 MiB / 100k chars; source + text SHA-256. GET form: one URL via ?url=; use POST for batches or render.

PDF text extraction — extract text from a public digital PDF into page-numbered JSON for RAG (no OCR). $0.01 USDC Base or Algorand. Caps 20 pages / 5 MiB / 100k chars; source + text SHA-256. No passwords or table reconstruction. Failed extractions not settled.

Extract structured JSON from a web page or text with fields you define: send url (or text) and fields like {"price": "price in EUR as number", "in_stock": {"type": "boolean"}}, get back exactly those fields, typed and validated against a JSON schema. Types: string, number, integer, boolean, string[], number[]. Fields the content does not state come back as null instead of being guessed. Up to 25 fields; optional instructions for extra rules.

Data Quality Gate: deterministic dataset quality verdict

Structured data extraction with local AI: give a text and a list of field names, get a JSON object with those fields (null when absent).

Calculates each group positive-outcome rate, compares it with an explicit or automatically selected reference group, and reports rate differences and selection-rate ratios. Aggregate disparity metrics do not establish discrimination, fairness, causation or legal compliance and can conceal intersectional or sampling effects.

Calculates each group positive-outcome rate, compares it with an explicit or automatically selected reference group, and reports rate differences and selection-rate ratios. Aggregate disparity metric…

Compares scalar confidence with binary correctness, calculates mean squared Brier loss, partitions confidence into caller-selected equal-width bins and reports expected and maximum calibration error. It does not validate labels, correct class imbalance or prove calibration beyond the supplied sample.

Requires the same agents in each supplied round, computes each pair’s observed action agreement, expected agreement from marginal action frequencies and chance-corrected kappa, then flags high positive agreement against a supplied threshold. Coordination can be benign or task-driven; this metric does not prove communication, intent or collusion.

Aligns caller-labeled dimensions, computes cosine similarity and a normalized L1 difference, and flags drift against a supplied cosine threshold. It does not infer goals from behavior, decide which goal is correct or detect deception; results depend entirely on the supplied vector representation.

Tokenizes each claim and its supplied evidence, removes a small declared English stop-word list, and reports the fraction of unique claim tokens present in evidence. Coverage below a supplied threshold is flagged. This is lexical grounding screening only: overlap does not prove truth, low overlap does not prove hallucination, and the service does not retrieve or validate evidence.

skills: {'id': 'check_health', 'name': 'Check Health', 'description': 'Site availability, robots.txt, AI crawler access (GPTBot, ClaudeBot, PerplexityBot), sitemap presence', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_llms_txt', 'name': 'Check Llms Txt', 'description': 'llms.txt existence and quality check: sections, links, statistics', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_schema', 'name': 'Check Schema', 'description': 'Schema.org JSON-LD detection: types found, FAQ schema, Organization schema', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_agent_files', 'name': 'Check Agent Files', 'description': 'openapi.json, ai-plugin.json, x402 payment support detection', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_mcp', 'name': 'Check Mcp', 'description': 'MCP discoverability: /.well-known/mcp.json, agent.json, MCP Server Card', 'tags': ['audit', 'mcp', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'score_aeo', 'name': 'Score Aeo', 'description': 'Full AEO (AI Search Visibility) score 0-100 with detailed checks', 'tags': ['score', 'ai', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'score_geo', 'name': 'Score Geo', 'description': 'Full GEO (Citation Readiness) score 0-100 with detailed checks', 'tags': ['score', 'ai', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'score_agent', 'name': 'Score Agent', 'description': 'Full Agent Readiness score 0-100 with detailed checks', 'tags': ['score', 'ai', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}; conformance: fail

skills: {'id': 'check_health', 'name': 'Check Health', 'description': 'Site availability, robots.txt, AI crawler access (GPTBot, ClaudeBot, PerplexityBot), sitemap presence', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_llms_txt', 'name': 'Check Llms Txt', 'description': 'llms.txt existence and quality check: sections, links, statistics', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_schema', 'name': 'Check Schema', 'description': 'Schema.org JSON-LD detection: types found, FAQ schema, Organization schema', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_agent_files', 'name': 'Check Agent Files', 'description': 'openapi.json, ai-plugin.json, x402 payment support detection', 'tags': ['audit', 'http', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'check_mcp', 'name': 'Check Mcp', 'description': 'MCP discoverability: /.well-known/mcp.json, agent.json, MCP Server Card', 'tags': ['audit', 'mcp', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'score_aeo', 'name': 'Score Aeo', 'description': 'Full AEO (AI Search Visibility) score 0-100 with detailed checks', 'tags': ['score', 'ai', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'score_geo', 'name': 'Score Geo', 'description': 'Full GEO (Citation Readiness) score 0-100 with detailed checks', 'tags': ['score', 'ai', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}, {'id': 'score_agent', 'name': 'Score Agent', 'description': 'Full Agent Readiness score 0-100 with detailed checks', 'tags': ['score', 'ai', 'x402', 'ai-visibility'], 'examples': [], 'input_modes': None, 'output_modes': None}; conformance: fail