qa-automation-mcp-plugin

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vtable_get_column_values

Reads cell values from specified columns using Chinese column titles, returning either visual text or raw values, and identifies missing columns.

Instructions

按中文列标题读取该列所有单元格的值。titles 为列标题数组（如 ["商品名称","商品编码"]）；raw=false 读取场景图渲染后的视觉文本（与界面显示一致），raw=true 读取原始字段值（如数字码/状态码）。返回每列值列表及缺失列。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "titles": "<titles>"
    },
    "name": "vtable_get_column_values"
  }
}

Input Schema

Name	Required	Default
`raw`	No
`titles`	Yes
`iframe_selector`	No	div[aria-hidden=false] iframe

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 5, 2026

TDQS

A4.4/5.0

Behavior4/5

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 a significant behavioral nuance: raw=false returns rendered visual text whereas raw=true returns raw field values, and it mentions the return includes missing columns. It does not state explicit side effects or prerequisites but implies a read-only operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main purpose, and packed with essential information without any fluff or repetition. It effectively explains the tool's function, parameters, and return behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core purpose, key parameters, and return summary, while the output schema provides formal return structure. It lacks explicit prerequisites or exclusions, but given the tool's relatively simple read nature, it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by explaining the key parameters: titles (array, with example) and raw (boolean, with behavioral difference). It omits iframe_selector, but that parameter has a default and is less user-facing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reads all cell values in a column by Chinese title, using specific verbs and resource. This distinguishes it from sibling tools like vtable_get_cell_text (single cell) and vtable_get_all_records (all records).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use: to retrieve column values by title, with an explanation of the raw parameter's behavior. It does not explicitly name alternatives or exclusion cases, so it misses the top score but is still clear.

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