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table_manage

Create, list, preview, render, and evolve table schemas with operations to add, remove, or rename columns. Export results to Excel or Markdown for structured data analysis from PDF assets.

Instructions

📊 表格管理工具：建立、刪除、列表、預覽、渲染、Schema 演進。

Operations:

create: 建立新表格
delete: 刪除表格
list: 列出所有表格
preview: Markdown 預覽
resume: 恢復工作（Token-efficient）
render: 渲染為 Excel/Markdown
add_column: 新增欄位
remove_column: 移除欄位
rename_column: 重新命名欄位

Args: operation: 操作類型 intent: [create] comparison / citation / summary title: [create] 表格標題 columns: [create] 欄位列表 [{"name":"Drug","type":"text"}] source_description: [create] 資料來源 table_id: [大部分操作] 表格 ID limit: [preview] 預覽行數 format: [render] 輸出格式 filename: [render] 檔案名稱 column_name: [add/remove/rename_column] 欄位名 column_type: [add_column] 欄位類型 required: [add_column] 是否必填 default_value: [add_column] 預設值 enum_values: [add_column] enum 可選值 new_name: [rename_column] 新欄位名

Examples: table_manage("create", intent="comparison", title="Drug Compare", columns=[{"name":"Drug","type":"text"}]) table_manage("list") table_manage("preview", table_id="tbl_xxx") table_manage("add_column", table_id="tbl_xxx", column_name="Route", column_type="enum", enum_values=["IV","IM"])

Input Schema

TableJSON Schema

Name	Required	Default
`limit`	No
`title`	No
`format`	No	excel
`intent`	No
`offset`	No
`columns`	No
`filename`	No	output
`new_name`	No
`required`	No
`table_id`	No
`operation`	Yes
`column_name`	No
`column_type`	No	text
`enum_values`	No
`artifact_only`	No
`default_value`	No
`source_description`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.4.0Sep 20, 2026
- removedInput schema / properties / default_value / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```

Changed1 schema field changedv1.0.1Aug 19, 2026

removedInput schema / properties / ctx

Removed value: -{
-  "anyOf": [
-    {},
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Ctx"
-}

First observedv0.7.0Jun 7, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are present, so the description carries full responsibility for revealing side effects. It discloses almost nothing about consequences: delete and remove_column are listed by name but with no warning about data loss or reversibility, and there is no mention of persistence, authorization, or side-effect profile. The only behavioral trait added is 'resume' being token-efficient, which is far too little for a mutation-heavy tool.

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 organized into summary, operations, args, and examples, with the core purpose front-loaded. The bullet lists are scannable and each entry carries operational meaning. Although long, the length is proportionate to a 9-operation dispatcher and the examples earn their place.

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

Completeness3/5

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

For a tool with 17 parameters and 9 operations, the description covers operation semantics, per-operation args, and examples, and an output schema exists to document return values. However, it omits the destructive behavior of delete and remove_column, the meaning of offset and artifact_only, and a required-parameter matrix per operation. These gaps matter for safe, correct invocation, so completeness is only partial.

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?

Schema description coverage is 0%, but the Args section maps nearly every parameter to the operation(s) that use it, e.g., intent for create, table_id for most operations, and enum_values for add_column. It also provides a concrete JSON example for columns and a full add_column example. It omits offset and artifact_only, so the compensation is strong but not exhaustive.

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

Purpose4/5

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

The description opens with a clear umbrella statement (建立、刪除、列表、預覽、渲染、Schema 演進) and enumerates nine operation verbs, each tied to a resource (表格/欄位). This makes it clear the tool manages table lifecycle and schema. It doesn't explicitly differentiate it from sibling table tools like table_data, table_cite, or docx_table, so it stops short of a 5.

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

Usage Guidelines3/5

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

Operation-specific arg annotations ([create], [preview], [render], etc.) and examples imply when each operation is used, and the 'resume' entry hints at token-efficient workflows. However, there is no explicit guidance on when to choose table_manage over sibling table tools, and no when-not or exclusion conditions. This is adequate but not explicit routing.

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