vtable_cell_info

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

While no annotations are provided, the description directly signals a read-only behavior through '读取' and enumerates exactly what information will be collected, including value, behavior classification, editability, center point, and viewport status. It also frames the tool as a verification mechanism, implying no state mutation. It could go further by mentioning error edge cases or whether the read has any layout/scroll side effects, but for this simple read tool the behavioral disclosure is adequate.

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: one sentence states the tool's function and output scope, and a second sentence gives the practical verification-loop usage pattern. There is no filler, and the most important information is front-loaded.

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 output schema covers return-value details, and the description explains what the tool reads and how to use it in before/after interaction verification. It is largely complete for a simple single-cell lookup, though it could be more complete by noting when to prefer sibling tools such as vtable_read_cells for bulk reads or vtable_meta for table-level metadata.

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 schema has 0% description coverage, and the description does not explicitly explain 'col' or 'row'. However, the parameter names are inherently clear as grid coordinates, and the surrounding '单元格' (cell) context makes their role obvious. The description adds no coordinate-system details or constraints, leaving only a minor 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 description opens with a specific action and resource: '读取 VTable 单元格信息' (read VTable cell information), and lists concrete aspects it returns: value, behavior classification, editability, center point, and viewport visibility. This clearly separates it from click/drop/analysis tools, though it does not explicitly distinguish it from similarly named siblings like vtable_cell_resolve or vtable_read_cells.

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 explicit usage guidance: call once before and once after an interaction so the AI can confirm the target and the result, referencing the Playwright MCP verification-loop idea. This is strong contextual guidance, but it does not name alternatives or state when not to use the tool.

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