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Moltline Vision Maths

Image Probe

image_probe
Read-onlyIdempotent

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": ""} (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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_base64YesThe image file, base64-encoded. The first few hundred bytes are enough for every supported format; send a prefix rather than a large file. Data-URL prefixes like "data:image/png;base64," are accepted and stripped.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses error behavior: 'never raises a protocol error — it returns {"error": ...}'. It also explains the underlying mechanism ('nothing is decoded', 'bytes_inspected') and retry safety. No contradiction exists with annotations; in fact, the description reinforces them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well structured: a one-line summary, a compact explanation with an example, usage guidance, error behavior, and safety note. Every sentence carries information; there is no fluff or repetition. The length is justified by the richness of the content, and it is front-loaded with the purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter, strong annotations, and an output schema, the description is fully complete. It covers input (base64 prefix), output (format, dimensions, derived values), error semantics, limitations (no pixel/EXIF), and retry behavior. There are no unanswered questions an agent would need to resolve before invoking.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage for data_base64, including the prefix strategy and data-URL handling, so the description doesn't need to add much. It does add a concrete example ('{"data_base64": "iVBORw0KG..."}') and restates the prefix guidance in context, which slightly enhances usability without redundancy. A 4 is appropriate given the schema's strength.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Read an image's format and pixel size from its header alone.' This clearly distinguishes it from sibling tools like resize_plan (planning) and detection_metrics (metrics), and the 'header alone' scoping prevents overreach. The FREE note and supported formats further pin down its exact function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use: 'Use to find out what you are dealing with before planning a resize.' It also gives clear exclusions: 'Not for pixel content - nothing here reads pixels - and not for EXIF.' This tells the agent precisely when to invoke and when to avoid, and it hints at the resize_plan sibling without needing to name it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.6/5.0
Disambiguation5/5

Each of the six tools addresses a clearly distinct operation: bbox_convert handles format transforms, nms removes duplicate detections, detection_metrics evaluates predictions, image_probe reads headers, resize_plan computes resize parameters, and colour_check checks WCAG contrast. There is no overlap or ambiguity between them.

Naming Consistency3/5

Most tools follow a noun_verb pattern (bbox_convert, colour_check, image_probe, resize_plan) but two are outliers: detection_metrics is noun_noun and nms is an acronym. Naming is readable but not fully consistent across the set.

Tool Count5/5

Six tools is a well-scoped count for a vision/maths utilities server. Each tool covers a distinct, necessary facet of computer vision workflows (conversion, suppression, metrics, probing, planning, contrast), with no apparent bloat or trivial tools.

Completeness4/5

The tool surface covers the core calculation needs for bounding-box-based vision tasks: conversion, NMS, evaluation, image metadata, resize planning, and accessibility contrast. Missing features like actual image manipulation or geometric transforms are outside the stated 'maths' scope, so coverage is strong. One could argue for a tool that applies resize to boxes, but resize_plan already returns the transform parameters, which is sufficient.

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