understand_image

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

With no annotations provided, the description carries the burden of disclosing behavior. It reveals the underlying model (glm-4.6v-flash), input types (local path or URL), single-image vs. multi-image distinct behaviors, and language of answers (Chinese). This goes beyond basic operation and provides useful context, though it doesn't mention failure modes or 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?

The description is a single, well-structured sentence that uses semicolons to separate key behaviors. It front-loads the main purpose, includes necessary details (model, input types, single/multi behavior, question) without any fluff or repetition. Every clause adds useful 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?

The description is sufficiently complete for an image understanding tool with only two parameters. It covers input formats, single vs. multiple handling, and optional question support. Since an output schema exists, return values are structured elsewhere. It lacks details on known limitations or edge cases, but overall provides a solid contextual picture.

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. It explains that 'images' can be local file paths or HTTP(s) URLs, and 'question' is an optional specific question to be answered in Chinese, which adds meaning beyond the raw schema. It also clarifies the behavior of images (single vs. multiple) that relates to parameter usage.

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 uses a vision model to understand one or more images, with specific behavior for single vs. multiple images. It names the resource (images) and the action (understand/describe). No sibling tools exist, so no differentiation is needed, but the verb 'understand' is slightly generic, though detailed description is mentioned.

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 provides clear context on when to use the tool: for understanding images via local paths or URLs, and optionally asking a specific question. It doesn't explicitly state exclusions or alternatives, but since no sibling tools are listed, this is acceptable. The behavior for single vs. multiple images is also a form of usage guidance.

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