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read_records

Read a Google Sheets tab as labelled records using a header row, returning each row's sheet row number for later updates.

Instructions

Read a tab as labelled records, using one row as the column headers.

Each record includes a _row field with its 1-based sheet row number, which you can use to build an A1 range for a later update.

Args: spreadsheet_id: The long token from the spreadsheet URL. sheet_name: The tab name, e.g. "Sales". header_row: 1-based row number holding the column headers. limit: Maximum number of data records to return.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
header_rowNo
sheet_nameYes
spreadsheet_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose a real behavioral trait not in the schema: every record carries a 1-based `_row` field for building A1 ranges. It says nothing about permissions, pagination beyond `limit`, or behavior when header_row is wrong or the tab is empty.

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?

Purpose is front-loaded in the first sentence, the `_row` note is a genuinely useful second sentence, and the Args block earns its place because schema coverage is 0%. Slightly longer than minimal but no filler.

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?

An output schema exists, so return values need no explanation, and all four parameters are documented. What is missing is explicit routing among the many sibling read tools, which is the one gap for an agent choosing between this and read_range.

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%, so the description must compensate, and it documents all four parameters. It adds genuine meaning beyond the schema titles: spreadsheet_id is the long token from the URL, sheet_name is the tab name with an example, and header_row is the 1-based row holding headers. Only the `limit` default (200) is left unstated.

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?

States a specific verb and resource with a distinguishing qualifier: reads a tab as labelled records keyed by a header row, which separates it in spirit from read_range's raw range read. However, it never names read_range or find_rows explicitly, so sibling differentiation is left implicit.

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?

The mention of the `_row` field 'which you can use to build an A1 range for a later update' implies a read-then-update workflow, giving implied usage context. But there is no explicit statement of when to prefer this over read_range or find_rows, and no exclusions.

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