# Moltline Vision Maths

> Image header probing, bbox conversion, resize plans and colour maths. 4 of 6 free.

- name: com.moltlinestudio/vision
- version: 1.2.0
- connection class: R0 (autonomous)
- trust: 83/100 flags: duplicate-repo
- quality: 88/100 (good)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/com.moltlinestudio%2Fvision
- tools: https://api.protogrid.dev/v1/servers/com.moltlinestudio%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://mcp.moltlinestudio.com/vision"
    }
  }
}
```


## Trust

- hygiene: 60
- liveness: 96
- freshness: 85
- provenance: 85
- drivers: +repository +dns-namespace +website +reachable +uptime-91% ~updated-42d-ago -duplicate-repo(22 names)
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 88/100 (good)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### 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: 93 (weight 30)

- pass `tools.descriptions`: every tool has a description
- warn `tools.description_length`: 1 tools have descriptions over 1,024 characters, which crowds the context
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 6 of 6 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 2,771 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 100 (weight 15)

- pass `stability.changes`: no tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

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

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

- tools: 6, about 2,771 tokens to load them all
- tool set hash: a5cb7f4f3719adb7b65f09502ad87ba2
- badge: [![protogrid quality](https://protogrid.dev/badge/com.moltlinestudio/vision.svg)](https://protogrid.dev/servers/com.moltlinestudio/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://mcp.moltlinestudio.com/vision (streamable-http, auth none, reachable true, uptime30d 0.90654206)

## Packages

_none_

## Tools (6)

- `bbox_convert`: Convert bounding boxes between COCO, Pascal VOC and YOLO. FREE.

The three formats disagree on everything: COCO is [x, y, width, height],
VOC is [x1, y1, x2, y2], YOLO is [cx, cy, w, h] normalised to the image.
Getting this wrong produces boxes that look plausible and quietly ruin
every metric. Typical input {"boxes": [[10, 20, 100, 50]], "from_format":
"coco", "to_format": "yolo", "image_width": 640, "image_height": 480}
returns {"boxes": [[0.0938, 0.0938, 0.1562, 0.1042]], "converted": 1,
"rejected": []}.

Use whenever a dataset and a model disagree about format. Not for scoring
predictions (detection_metrics) and not for removing overlaps (nms). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "boxes must contain at least one box"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
- `colour_check`: Check a colour pair against the WCAG contrast thresholds. FREE.

Uses the WCAG 2 relative-luminance formula, so the number matches what an
accessibility audit will report. Typical input {"foreground": "#767676",
"background": "#ffffff"} returns {"contrast_ratio": 4.54, "AA": true,
"AAA": false, "required": {"AA": 4.5, "AAA": 7.0}, "verdict": "Passes AA
for normal text, fails AAA."}.

Use when generating or auditing an interface. Not for converting colours
between spaces and not for palettes. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "foreground must be a hex colour like #767676"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
- `detection_metrics`: Score detections against ground truth and show the working. PREMIUM (license).

Greedy matching at the IoU threshold, highest-confidence prediction first,
each ground-truth box matched at most once - the standard protocol.
Reports per-class precision, recall and F1, and average precision by the
all-points interpolation used by Pascal VOC 2010 onward. Typical input
{"predictions": [{"box": [0,0,10,10], "label": "cat", "score": 0.9}],
"ground_truth": [{"box": [1,1,11,11], "label": "cat"}]} returns
{"overall": {"tp": 1, "fp": 0, "fn": 0, "precision": 1.0, "recall": 1.0,
"f1": 1.0}, "per_class": {...}, "mAP": 1.0}.

Use to compare two models on the same held-out set. Not for cleaning up a
single model's overlapping output first - run nms before this. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "ground_truth must contain at least one box"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
- `image_probe`: Read an image's format and pixel size from its header alone. FREE.

Dimensions live in the first few dozen bytes of PNG, JPEG, GIF, BMP and
WebP, so a base64 prefix is enough - you do not need to send the whole
file, and nothing is decoded. Typical input {"data_base64": "iVBORw0KG..."}
returns {"format": "png", "width": 1920, "height": 1080, "aspect_ratio":
1.7778, "aspect_label": "16:9", "megapixels": 2.07, "orientation":
"landscape", "bytes_inspected": 512}.

Use to find out what you are dealing with before planning a resize. Not for
pixel content - nothing here reads pixels - and not for EXIF. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "data_base64 must not be empty"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
- `nms`: Remove duplicate detections of the same object. PREMIUM (license).

Greedy non-maximum suppression: keep the highest-scoring box, drop
everything overlapping it above the threshold, repeat. Ties break on the
earlier index, so the result is deterministic rather than dependent on sort
stability. Typical input {"boxes": [[0,0,10,10],[1,1,11,11],[50,50,60,60]],
"scores": [0.9, 0.8, 0.7]} returns {"keep": [0, 2], "suppressed": [{"index":
1, "by": 0, "iou": 0.6807}], "kept": 2}.

Use after a detector that emits overlapping boxes. Not for scoring against
ground truth (detection_metrics) and not for format changes (bbox_convert). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "boxes and scores must be the same length"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
- `resize_plan`: Work out the exact scale, padding and crop for a model input size. FREE.

Returns the numbers you need to transform boxes alongside the image, which
is the step that usually gets skipped. Typical input {"width": 1920,
"height": 1080, "target": "yolo_640"} returns {"scale": 0.3333,
"resized": [640, 360], "pad": {"left": 0, "top": 140, "right": 0,
"bottom": 140}, "box_transform": "x_new = x * 0.3333 + 0; y_new = y *
0.3333 + 140"}.

Use before feeding an image to a fixed-input model. Not for finding out the
image's size in the first place - that is image_probe. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown target <value>; use one of <value> or set"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.