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describe_columns

Profiles sheet or range columns like a data table: detects headers, infers data types, counts non-null/empty/distinct values, min/max, common text, errors, and formula/constant status.

Instructions

Profile the columns of a sheet or range like a data table: detected header row, inferred type per column (integer, number, text, date, datetime, boolean, mixed ...), non-null and empty counts, distinct count, min/max, most common text values, numbers stored as text, error cells, and whether the column holds formulas, constants or a MIX of both (a sign someone overwrote a formula with a typed number).

header_row: omit to auto-detect; 0 means 'no header'; N means row N is the header (rows above it are ignored). range limits the area (default: the whole used range).

Input Schema

NameRequiredDescriptionDefault
pathYes
rangeNo
sheetYes
max_rowsNo
header_rowNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well: it discloses the profiling output, header auto-detection, range defaults, and formula/constant/mixed detection. However, it does not mention max_rows truncation, performance characteristics, or read-only/error behavior.

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 front-loaded with the core purpose, then moves into compact parameter notes. Every clause contributes profiling detail or non-obvious input semantics, with no wasted wording.

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?

Given that an output schema exists, the description need not explain return values, yet it still adds useful output context. It covers the most non-obvious input behaviors, but leaves gaps around max_rows and sibling-tool selection.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must carry parameter meaning. It documents header_row very well and explains range's default, but it omits path, sheet, and especially max_rows, which can affect how much data is profiled.

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 verb and resource: "Profile the columns of a sheet or range like a data table." It also enumerates the profile dimensions clearly. It does not explicitly distinguish this tool from overlapping siblings such as read_range or check_formulas, so it falls 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 Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named despite sibling tools that could overlap, especially check_formulas and read_range. The header_row and range notes are parameter guidance, not routing guidance.

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