image_analysis

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

With no annotations, the description carries the full burden. It only documents input formats for image and question, but says nothing about return behavior, limitations, or side effects. This is a significant gap for a general image analysis tool.

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 extremely concise: one sentence for purpose/usage and a clean Args list. It is front-loaded with the key purpose and wastes no words.

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?

It covers purpose, usage context, and parameter semantics adequately. Since an output schema exists, return-value details are not required. However, the absence of any behavioral notes (e.g., limitations, error cases, or security considerations for URLs) leaves it only minimally complete.

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%, and the description compensates well by detailing image input types (local path, HTTP(S) URL, base64 URI) and clarifying question as '想了解的内容或问题' (content or question you want to know). This adds meaningful value beyond the schema's bare field names.

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 states '通用图像理解' (general image understanding) and positions it as a fallback for when specialized tools don't match, distinguishing it from siblings like extract_text_from_screenshot. The verb+resource is clear, though it does not explicitly name alternatives.

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?

It explicitly says '适用于其他专用工具不匹配的场景' (suitable for scenarios where specialized tools do not match), giving a clear condition for use. It also implies question answering via the question parameter, but lacks specific exclusions or named alternative tools.

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