ocr_layout_paddleocr_vl

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

No annotations are provided, so the description carries the full burden. It discloses backend details, output format (blocks with bounding boxes), language coverage, and a prerequisite CLI. Missing error behavior and performance caveats, but sufficient for a read-only extraction tool.

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 compact, starts with the primary purpose, and gives backend specs in a dedicated sentence. No redundant wording; every sentence adds value.

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?

While output format and backend compatibility are covered, the tool has 3 undocumented parameters and no output schema. Significant gaps remain around mode values, lang usage, and detailed return structure, making it incomplete for confident invocation.

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%, and the description provides no parameter explanations. lang and mode are entirely undocumented, and path is only implied as the input. The description fails to compensate for the missing schema metadata.

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?

Description clearly states the tool extracts text with layout analysis and returns blocks with bounding boxes. It names the backend (paddleocr_vl), which distinguishes it from sibling tools using other backends like paddle or vision.

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?

Provides clear context on when to use it: most accurate, supports 109 languages, tables/formulas/charts, and requires paddleocr-vl Swift CLI. It implies preference for maximum accuracy and complex content, though it doesn't explicitly name sibling alternatives or state when not to use it.

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