image_understand

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

With no annotations provided, the description explains that the server internally adds system vision prompts and calls a vision model, disclosing the processing flow. It also specifies image source methods and file constraints. Missing details on rate limits or authorization are acceptable for a read-like tool. The description is transparent about its operation.

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 well-structured with sections and bullet points, making it easy to scan. While it is somewhat verbose, each sentence serves a purpose (e.g., calling scenarios, source types, prompt rules). Minor redundancy could be trimmed, but overall it balances detail and clarity.

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?

The tool has no output schema, yet the description does not specify what the agent can expect as a result (e.g., text analysis, structured data). This omission leaves the agent unsure about the return value, which is critical for invocation decisions. The description covers input well but neglects output behavior.

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%, setting baseline at 3. The description adds value by elaborating on `image_source` (three methods, supported formats, size limit) and `prompt` (keep it simple, raw user query). These details enhance understanding beyond the schema's minimal descriptions.

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 identifies the tool as an image understanding tool with specific use cases (UI, code screenshots, etc.). It provides explicit criteria for when to invoke it ('用户提到“看图、看截图...') and emphasizes preference over text-only reasoning, making the purpose unambiguous.

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 includes a dedicated '何时调用' section with concrete examples of user requests. It also advises against constructing complex prompts, directing to pass the user's raw question. Although no exclusion cases are listed, the tool's scope is well-defined and sufficient for the agent to decide.

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