vision_inspect

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

No annotations are provided, so the description carries the full behavioral burden, and it delivers little beyond input mechanics. It never says what the tool returns (text answer), whether it is read-only, what happens on model failure, or why a default model is pre-selected — all relevant for a remote vision inference call.

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?

Two tight sentences, front-loaded with the capability before the input modes; nothing is padded. It loses a point only because the second sentence enumerates two of three input paths rather than being complete.

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?

With 7 parameters, no required fields, no annotations, and no output schema, the description is thin. It never explains the task enum (ui_structure, ui_bug, chart), the model override, or what happens if no image parameter is supplied.

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 coverage is 100%, so the baseline is 3. The description adds one genuinely useful semantic: that image_base64 must be paired with mime_type, and that image_path means a local file — but it omits image_url, a third input mode present in the schema.

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?

States a specific verb pair (describe / answer questions) and the resource (an image), so the core capability is unambiguous. It does not distinguish itself from siblings like vision_ocr, vision_annotate, or vision_compare, which an agent must differentiate between in this cluster.

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 is no guidance on when to pick this tool over the five sibling vision_* tools, and no exclusions (e.g., 'use vision_ocr for text extraction'). The description only tells the agent how to supply an image, not when this tool is the right choice.

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