text_detection

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly states the core behavior, but does not disclose the return format, coordinate conventions, confidence scores, or failure modes. For a simple non-mutating detection tool this is acceptable but not rich.

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?

One clean, front-loaded sentence that states the verb and output object with zero filler. Every word contributes to understanding the tool's purpose.

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?

For a one-parameter detection tool with no output schema, the description conveys the essential outcome: text regions and bounding boxes. It is complete enough for basic invocation, though it would benefit from a brief note about returning coordinates and when to use ocr_recognize instead.

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?

The single parameter 'image' is already fully documented by the schema as 'Base64 encoded image data' (100% coverage). The description adds no additional semantic detail about format requirements, size limits, or accepted encodings, so it stays at the baseline.

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 uses a specific verb ('Detect'), names a clear resource ('text regions and bounding boxes in an image'), and implicitly distinguishes this from sibling ocr_recognize by focusing on geometry rather than text extraction. An agent can immediately understand what the tool does.

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 gives no guidance about when to choose text_detection over ocr_recognize or the other siblings. There is no explicit 'use this when detection is needed' or 'use ocr_recognize when text extraction is needed', leaving the agent to infer selection criteria.

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