Vision Eyes — локальный разбор изображения

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

No annotations are provided, so the description carries the full disclosure burden. It clearly states local-only operation, no API keys/payments, the Pillow+numpy dependency, the optional local-ai install for classify, and the coordinate convention (x left→right, y top→bottom). It does not mention error cases or what happens when no image source is provided, but the main behavioral traits are transparently disclosed.

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

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

The description is dense but every sentence earns its place: value proposition first, then per-action behavior, then input formats, then coordinate conventions. It is a single run-on paragraph rather than structured bullets, which slightly hurts scanability, but there is no filler or repetition.

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

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

For a complex multi-action tool with 19 optional parameters, the description covers the action vocabulary, image input variants, the classification label contract, dependency requirements, and coordinate rules. Since an output schema exists, not detailing return values is acceptable. A minor gap is that it doesn't specify which parameters are required per action (e.g., compare needs a second image), but overall the guidance is sufficient.

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

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

Schema description coverage is 100%, setting a baseline of 3, but the description adds meaning beyond the terse per-parameter schema text: it explains the three interchangeable input sources (image_path/image_url/image_base64), the labels format with concrete examples, and which action consumes which parameters. This meaningfully helps an agent map parameters to intents.

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

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

The description opens with 'LOCAL image eyes for AI agents' and sharply enumerates six concrete actions (analyze, pixel, crop, grid, compare, classify), each with its own output. It states exactly what the tool does — local image parsing with no cloud — and is specific enough to stand alone even without sibling tools to differentiate from.

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

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

There are no sibling tools, so no alternative selection is needed, but the description gives clear per-action usage context: pixel for exact color at coordinates, crop 'so the agent can look closer', classify with explicit label examples. It also flags the optional local-ai install requirement for classify, which is exactly the kind of conditional guidance an agent needs.

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