ask_vision

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

With no annotations, the description carries the full burden. It usefully discloses that calls go to an external provider (Anthropic, OpenAI-compatible servers, Gemini) and that canvas scope can be narrowed to a region or single layer, implying a read-oriented operation. It omits critical operational context: whether API keys/backends must be configured, latency/cost, what happens when no region is given (full canvas?), and whether the call has any side effects.

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?

Roughly three sentences: purpose and output forms are front-loaded, followed by the provider enumeration. There is little waste, though the provider list is dense and could be better separated from the capability statement.

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 nine-parameter tool with no annotations and no output schema, the description covers purpose and provider choice but leaves credential setup, default capture scope, image downscaling behavior, and return-shape expectations unstated. It is workable but not complete enough that an agent could invoke it confidently without trial and error.

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 0%, so the description must compensate for nine undocumented properties. It explains provider (with concrete values), model (implicitly via Claude/GPT/Gemini), and that a region or single layer can be targeted, which maps loosely to x/y/width/height/layer. It says nothing about max_size (default 1024), coordinate units, or how width/height interact with x/y, leaving several parameters unexplained.

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 uses a specific verb and resource ('Ask another vision AI about the canvas and get its answer as text') and enumerates the output kinds (critique, caption, image-model prompt). It implicitly differentiates from local siblings like analyze_image or read_pixels by emphasizing 'another vision AI', but never names those alternatives explicitly, so an agent must infer the split.

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?

The three listed use cases (second opinion/critique, caption, prompt generation) give implied usage context. However, there is no explicit when-not guidance, no mention of how this differs from local analysis tools in the sibling list, and no mention of prerequisites like configured API credentials.

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