qwen_vision

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

No annotations are provided, so the description carries the burden. It adds useful context by naming the specific model and explaining the default prompt behavior (150-word description). However, it does not disclose potential error conditions, rate limits, or detailed return format, which leaves gaps given the absence of annotations.

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 a single, well-structured sentence that front-loads the core purpose, model name, and input types. No redundant words, and it efficiently covers the essential information.

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?

Given the moderate complexity and the presence of a detailed schema, the description sufficiently covers the tool's purpose and default behavior. It does not have an output schema, but the phrase 'answer questions about the image' adequately implies the return value. Minor gaps remain on error handling/edge cases, so it isn't a perfect 5.

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%, with all four parameters already explained in the schema. The description adds the model name and overall purpose but does not enrich the understanding of individual parameters beyond what the schema provides. Hence a baseline score of 3 is appropriate.

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 clearly states the tool's function: using the qwen3-vl-plus vision model to understand an image and answer questions about it. It specifies both the verb (understand/answer) and resource (image), and by emphasizing image input it distinctly separates itself from sibling tools qwen_search and qwen_chat.

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 description implies usage for image-related tasks by stating it handles local file paths or URLs and answers image questions. It gives a clear context but does not explicitly mention alternatives or exclusions; thus it stops short of a perfect score.

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