Calibrated vision for AI agents: meal nutrition, personal color, styling reports. x402, no API key.
| Endpoint | https://mart402.com/mcp/vision (streamable-http) |
|---|---|
| Registry version | 1.0.0 |
| Now | 9 tools · grade C (65) · pin ts_fd75c847a72931b7ade26646e0769949 · protocol 2025-06-18 |
| First / last listed | 2026-11-07 00:29 UTC / 2026-11-07 00:29 UTC |
| Grade over time | 2026-11-07: C (65), 9 tools |
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Tools: get_catalog get_receipt get_sample portrait_precheck meal_vision style_profile style_report report_pack portrait_analysis
get_catalog | Mart402 product catalog: all products, prices, payment config, error codes, rate limits. Free. params: none |
get_receipt | Re-fetch a previously paid result for free by receipt id (every paid response carries receipt_id). params: receipt_id* |
get_sample | Free recorded sample response for a paid product (from real production runs, marked sample:true) - see the exact response shape before paying anything. Available: document-pipeline, enrich, extract, jp-bonus-net, jp-business-days, jp-company, jp-consumption-tax, jp-crypto-tax, jp-dependent-threshold, jp-mortgage, jp-overtime-wage, jp-payroll-net, jp-payroll-suite, jp-payslip-audit, jp-retirement-t params: product* |
portrait_precheck | FREE analyzability check before paying for portrait_analysis: is a face detectable, is the photo quality good enough (clipping/color cast)? Deterministic, no LLM. 10/hour/IP. params: image_url, image_base64 |
meal_vision | Analyze a meal photo to itemized nutrition JSON: dishes, portions, kcal, macros with per-item confidence, reference-object scale calibration, scene-complexity detection, a disclosed calibrated kcal range, PFC balance ratios and alcohol-aware 4/4/9 cross-checks. Calibrated on 30 weighed meals (range contained truth in 26/30). Every call is a 3-run consensus with disclosed spread + item agreement. $ params: image_url, image_base64, hints, pack_code, x_payment |
style_profile | Integrated styling verdict: seasonal color (measured colorimetry consensus) + face-impression zone (published geometry rules) + body frame + integration rules (palette subset, taste, silhouette, modifiers). 1-3 face photos, optional full-body + questionnaire. Flat $0.05/call. Choose this when you want the integrated styling analysis as JSON to use or render yourself; it is the same analysis that s params: image_url, image_base64, images, body_image_url, body_image_base64, questionnaire, style, x_payment |
style_report | Client-ready deliverable: the style-profile analysis rendered as a finished PDF report in English or Japanese (palette with color names, style-aware outfit formulas, virtual draping boards with disclosed candidate conflicts, honest confidence) returned as report_pdf_base64 + full profile JSON. For agents serving human end-clients. Flat $2.99/call, or $1.99/unit with a prepaid report_pack credit (p params: image_url, image_base64, images, style, name, body_image_url, body_image_base64, questionnaire, language, pack_code, x_payment |
report_pack | Volume pricing for style_report: one $19.90 payment returns 10 single-use credit codes ($1.99/unit vs $2.99 single, en or ja, no expiry). Treat codes like cash. Not an analysis: it buys prepaid style_report credits. Choose this only when you expect to produce several style_report PDFs; pass each code as pack_code to style_report. params: x_payment |
portrait_analysis | Personal color analysis from 1-3 portraits: measured pixel colorimetry (CIELAB/ITA) cross-checked against vision-model attributes, cross-photo lighting consistency, then a published deterministic rule table gives the 4-season verdict with palette and style advice. No identification; images not stored. Flat $0.05/call. Choose this when you need only the seasonal color verdict and palette as JSON. F params: image_url, image_base64, images, style, pack_code, x_payment |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.