ocr_formula_paddleocr_vl

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

With no annotations, the description carries the transparency burden. It discloses the backend, model size, hardware requirement (Apple Silicon), language support, and the prerequisite of the paddleocr-vl Swift CLI. However, it does not mention side effects, error behavior, or limitations beyond the hardware requirement.

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 highly concise and front-loaded, with the primary purpose in the first sentence and context in the second. Every sentence adds value, and there is no fluff or repetition.

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 tool is simple, but the description lacks critical details about parameters and usage context, and there is no output schema. It provides useful backend/hardware context but leaves the agent without enough information to fully invoke the tool correctly.

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 explanation for the three parameters (path, lang, mode). The parameter names are intuitive but the description does not clarify allowed values, defaults, or behavior, leaving agents to guess semantic meaning.

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's purpose: 'Extract mathematical formulas from an image. Returns LaTeX.' This uses a specific verb and resource, and the formula-focused scope distinguishes it from sibling OCR tools like text, layout, table, and chart extraction.

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 no direct guidance on when to use this tool versus the many siblings. It mentions 'Most accurate' and 'supports tables/formulas/charts' but does not explicitly state when to prefer this backend or how it compares to alternatives, leaving the AI agent without clear selection criteria.

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