# com.mart402/vision

> Calibrated vision for AI agents: meal nutrition, personal color, styling reports. x402, no API key.

- name: com.mart402/vision
- version: 1.0.0
- connection class: R0 (autonomous)
- trust: 83/100 flags: no-repository
- quality: 100/100 (new)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/com.mart402%2Fvision
- tools: https://api.protogrid.dev/v1/servers/com.mart402%2Fvision/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

```json
{
  "mcpServers": {
    "vision": {
      "type": "http",
      "url": "https://mart402.com/mcp/vision"
    }
  }
}
```


## Trust

- hygiene: 80
- liveness: 100
- freshness: 100
- provenance: 55
- drivers: +dns-namespace +website +reachable +uptime-100% +updated-0d-ago -no-repository
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 100/100 (new)

### protocol: 100 (weight 25)

- pass `protocol.modern`: supports 2026-07-28 (server/discover)
- pass `protocol.stateless`: answers without a session
- pass `protocol.transport`: streamable HTTP
- pass `protocol.list_ttl`: tool list is cacheable (ttlMs 300000)

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 9 of 9 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 2,437 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: n/a (weight 15)

- n/a `stability.changes`, `stability.rug_pull`: tool history recorded for 0 of the 7 days needed

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 9, about 2,437 tokens to load them all
- tool set hash: dc50e6bd00ae2f281f0407ff5509f61d
- badge: [![protogrid quality](https://protogrid.dev/badge/com.mart402/vision.svg)](https://protogrid.dev/servers/com.mart402/vision)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

_No tool definition changes recorded._

## Remotes

- https://mart402.com/mcp/vision (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## Tools (9)

- `get_catalog`: Mart402 product catalog: all products, prices, payment config, error codes, rate limits. Free.
- `get_receipt`: Re-fetch a previously paid result for free by receipt id (every paid response carries 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-tax, jp-social-insurance, meal-vision, ocr, parse, pdf-info, pdf-merge, pdf-pages, portrait-analysis, report-pack, structured, style-profile, style-report, verify.
- `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. $0.05/image.
- `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.
- `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.
- `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.
- `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.
- `style_report`: Client-ready deliverable: the style-profile analysis rendered as a finished English PDF report (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 (pass pack_code).