ocr_table_xiaomi

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

With no annotations, the description carries the full burden and does disclose useful operational context: local execution, model size, supported accelerators (MPS/CUDA/CPU), and output formats. But the trailing 'Markdown output, HTML tables, LaTeX formulas, key-information extraction' block appears to be generic model copy that conflicts with the earlier 'Returns structured CSV/JSON' claim, leaving the actual output behavior ambiguous for a table 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 core purpose and return format are front-loaded in the first two sentences, which is good. The remaining backend paragraph includes content (LaTeX formulas, key-information extraction) that is not relevant to table extraction and dilutes focus.

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?

There is no output schema, so the description must explain return values, and 'Returns structured CSV/JSON' is thin: it is unclear which format is returned, whether both are produced, or how to request one. The backend/hardware and language notes are helpful, but the output contract for a single-path-parameter tool remains underspecified.

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?

Only one parameter ('path') exists and schema coverage is 0%, so the schema adds nothing. The description implies it is an image path ('from an image'), but does not state accepted formats, local vs remote paths, or size limits, so it only partially compensates for the coverage gap.

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 first sentence gives a specific verb and resource ('Extract tables from an image'), which is unambiguous. It differentiates from the vision/paddle siblings only indirectly, via the backend traits (local 0.8B VLM, best structure fidelity, language coverage) rather than by naming when to prefer this over ocr_table_paddle or ocr_table_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?

The language notes ('Strong on Cyrillic, English, German and Japanese; Arabic/Korean are weak') and the 'Best structure fidelity' claim give an agent useful signals for choosing among the table backends. However, there is no explicit when-to-use/when-not statement or direct reference to the sibling table tools, so the routing decision is left partly to inference.

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