image_ocr_shrink

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

Without annotations, the description carries full burden. It adds context by mentioning 'clean' text and 'table markdown', and names the AI model. However, it does not disclose limitations (e.g., image size, URL requirements beyond schema), privacy, or exact processing behavior.

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 sentence, front-loaded with the action ('Extract'), and contains no filler. Every word adds value: 'clean' indicates quality, 'text and table markdown' specifies output formats, and the model name provides implementation context.

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 tool with a single parameter and no output schema, the description adequately covers purpose, output, and technology. It does not explain edge cases (e.g., supported image types, size limits), but for this simplicity, it is sufficiently 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?

The single parameter imageUrl is fully described in the schema as 'Public image URL to parse', providing complete coverage. The description adds no additional parameter-specific detail, so baseline 3 is appropriate.

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 explicitly states the tool extracts clean text and table markdown from images, using a specific verb and resource. This clearly distinguishes it from sibling tools like stealth_dom or airgap_scrub, which serve different purposes.

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 clearly implies when to use the tool (when you need text or markdown tables from images) and names the underlying model. It does not explicitly state alternatives or exclusions, but the use case is obvious given the tool name and description.

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