asset-aware-mcp

by u9401066

Overview Schema Source Related Servers Discussions Admin

Python

Local

table_data

Add, retrieve, update, and delete rows or cells in tables extracted from PDFs to manage structured data for research.

Instructions

📝 表格資料操作：新增/取得/更新/刪除列 & 儲存格。

Operations:

add_rows: 批次新增資料列
get_row: 取得單列（含引用）
update_row: 整列更新
delete_row: 刪除列
get_cell: 取得單格（含引用）
update_cell: 更新單格
clear_cell: 清除單格

Args: operation: 操作類型 table_id: 表格 ID rows: [add_rows] 資料列列表 row: [update_row] 新的列資料 row_index: [get/update/delete_row, cell ops] 列索引 (0-based) column_name: [cell ops] 欄位名 value: [update_cell] JSON 值；native 欄位使用 {kind, value} 保留型別

Examples: table_data("add_rows", "tbl_xxx", rows=[{"Drug":"A","Dose":1}]) table_data("get_row", "tbl_xxx", row_index=0) table_data("update_cell", "tbl_xxx", row_index=0, column_name="Drug", value="B")

Input Schema

TableJSON Schema

Name	Required	Description	Default
`row`	No
`rows`	No
`limit`	No
`value`	No
`offset`	No
`row_id`	No
`search`	No
`filters`	No
`table_id`	Yes
`operation`	Yes
`row_index`	No
`column_name`	No
`include_coverage`	No
`selected_columns`	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 / value / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
First observedv0.7.0Jun 7, 2026

TDQS

C2.9/5.0

Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure and provides some useful context: update_row is described as a full-row update, get_row/get_cell are said to include citations, and native field values preserve type via {kind, value}. However, it does not mention mutation side effects, reversibility, permissions, pagination, or what happens with deleted rows.

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

Conciseness4/5

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

The description is well-organized into operation list, argument mapping, and examples, making it easy to scan. It is somewhat verbose, but each section earns its place and the examples add practical clarity.

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

Completeness2/5

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

For a tool with 14 parameters and 9 schema-defined operations, the description is incomplete: it documents only 7 operations and provides no coverage of query/filter/pagination behavior. The presence of an output schema helps for return values, but the missing query_rows operation and multiple undocumented parameters leave important gaps.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It does explain operation, table_id, rows, row, row_index, column_name, and value, and includes useful examples. But it omits search, filters, limit, offset, row_id, include_coverage, selected_columns, and the query_rows operation, leaving several parameters semantically unexplained.

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 states a specific resource ('table data') and lists clear row/cell operations including add, get, update, delete, and cell-level variants. It is understandable on its own, though it does not explicitly differentiate from sibling table tools like table_manage or table_cite.

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

Usage Guidelines2/5

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

The description labels each operation and shows associated arguments and examples, but it never states when to prefer this tool over alternatives or when to choose one operation over another. Notably, query_rows is missing from the operation list despite being a valid enum value in the schema, leaving a usage gap.

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