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tabular_document_text

Extract cell ranges and table dimensions from an on-screen tabular document. Use for quick spot checks of headers, totals, or small tables rather than full exports.

Instructions

Return dimensions and a cells range of a tabular document field in the active window.

Use this for small tables or partial reads (headers, totals, spot checks). For large reports/tabular documents, prefer tabular_document_save/report export and analyze the saved PDF/XLSX/TXT/HTML file instead of reading UI cells one by one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
titleNo
compactNo
row_endNo
max_rowsNo
row_startNo
column_endNo
session_idNodefault-18
max_columnsNo
column_startNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.3

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It clearly indicates a read operation (returns dimensions and cells) and does not contradict any annotations. It adds context about the scope (small vs large) but does not mention any side effects or return format details. It is transparent enough for a read 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?

Two paragraphs, front-loaded with the purpose, followed by concise usage guidance. No unnecessary words; every sentence earns its place.

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?

With 10 parameters, no output schema, and no annotations, the description lacks critical details such as parameter semantics, return format, and how range selection works. The usage guidance is helpful but insufficient for an agent to call the tool correctly without additional documentation. The description does not cover the full context needed.

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 coverage is 0% and the description does not explain any of the 10 parameters. It only hints at partial reads (headers, totals, spot checks) without mapping to row_start/row_end, column_start/column_end, compact, max_rows, or max_columns. The agent is left to infer parameter meaning from names and defaults alone, which is insufficient for correct usage.

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 states a specific verb (Return), a specific resource (dimensions and a cells range of a tabular document field), and the context (active window). It clearly differentiates from sibling tools like tabular_document_save by mentioning the alternative for large reports, making its purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly states when to use (small tables, partial reads) and when not to (large reports), naming tabular_document_save/report export as alternatives. This gives the agent clear routing guidance.

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

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