recognize_image

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

Annotations already signal readOnlyHint=false, openWorldHint=true, and destructiveHint=false. The description adds that the tool calls an external Qwen multimodal model and that task determines the extraction behavior, which is useful contextual information beyond the annotations. It does not disclose latency, failure modes, or side effects, but for a recognition tool this is a reasonable level of transparency.

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 concise sentence that front-loads the core behavior: image input, URL support, task-driven processing, and the underlying model. Every part earns its place and there is no redundant wording.

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 tool's simplicity, complete parameter schema, existing output schema, and annotations that cover safety hints, the short description is mostly sufficient. It covers the essential input types and core behavior, though it could additionally mention when not to use it or any image format constraints. Overall, the available context is adequate for an agent to select and call the tool.

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%, so the schema already documents all three parameters: image, task, and mode. The description only reinforces that 'task' drives the model call, adding little beyond the structured schema. This aligns with the baseline of 3 for full schema coverage.

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 a specific verb ('recognize'), a clear resource ('local image or image URL'), and explains that recognition is driven by a task parameter invoking a Qwen multimodal model. It distinguishes itself from sibling tools by scoping to images rather than video or audio, though it does not explicitly name an alternative.

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 inputs by mentioning both local paths and URLs, and the sibling names make the video/audio alternatives inferable. However, it does not explicitly state when to use this tool over siblings or provide any exclusions, such as 'for videos, use recognize_video'.

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