Google Sheets

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Tools for working with Google Sheets against the Google Sheets API v4 (https://sheets.googleapis.com/v4/), with spreadsheet discovery via the Google Drive API. 26 tools across two complementary surfaces: a record surface — rows as objects keyed by their column headers (the "log this expense", "update the status to Done", "look up last quarter's total" jobs) — and a cell surface — raw A1-addressed values for formulas, precise numeric/text control, and arbitrary ranges. Plus spreadsheet/worksheet structure and presentation (formatting, sorting, validation).

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Independent, unofficial connector for Google Sheets. Not affiliated with, endorsed by, or sponsored by Google Sheets. "Google Sheets" is a trademark of its owner, used only to identify the service this connector works with.

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When to use this

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• Read or find data inside a spreadsheet — look up a row by a column value (lookupRow), find all matching rows (findRows), list a window of rows (listRows), or read a raw range (getValues).
• Write data — append a row or many rows (createRow / createRows), update specific columns of a row without disturbing the others (updateRow / updateRows), or write a raw range / formula (updateValues).
• Clear or delete — clear a row's contents but keep the row (clearRows / clearValues) or remove rows entirely (deleteRows).
• Manage structure — create a spreadsheet (createSpreadsheet), add / list / copy / rename / hide / delete worksheets, add columns.
• Format & validate — number/date/currency formats, text styling, sort a range, copy a range, dropdowns and number/date validation, conditional formatting.

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Setup

This is an agentskills.io skill.

If the connector has not been installed as a skill yet, install it first with npx skills add zapier/connectors --skill google-sheets (or your harness's own skill-install mechanism), then continue here. Installing the skill copies these files, not dependencies. Before running the CLI, a local MCP server, or zapier-sdk auth commands, run npm install --omit=dev here once. Importing the published package as a dependency in your own project instead? That npm install already resolves everything — see references/use-as-sdk.md.

Want the actual repo source instead — to browse references/, run this connector's tests, or hack on it? See README.md for a scoped git clone.

The connector runs on Node.js 22.18+. Pick the reference that matches how you're running it, and load it before doing anything else:

Scripts

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All scripts use the single google-sheets connection.

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Disambiguation & refusals

Disambiguation before a write. Before writing to a row you found by a column value (e.g. update a row located via lookupRow, or act on a spreadsheet found via listSpreadsheets), count the exact case-insensitive matches:

• Exactly one match — act on it. Don't over-ask; a single unambiguous match is the answer.
• Two or more that tie — stop. List the tied rows (or spreadsheets) with a distinguishing field (another column value, the spreadsheet's modified time / URL) and ask which one. Don't pick arbitrarily, and don't write to all of them.

Row numbers are not stable — they shift on insert/delete/sort. To target the same logical record across runs, match on a unique key column with lookupRow, not a remembered row number.

Confirm before destructive or bulk-overwrite operations. deleteWorksheet, deleteRows, clearRows, and clearValues remove data; updateValues can overwrite it. Confirm the exact target with the user first, and never delete / clear / overwrite more than asked.

Unsupported operations — say so and stop; don't fake it with another tool. This catalog deliberately does not:

• Create or manage charts, pivot tables, named ranges, protected ranges, or filters. There is no tool for these; don't approximate them with formatting.
• Find-or-create in one step. Compose it: lookupRow → if not found, createRow.
• Permanently delete a whole spreadsheet. Worksheet deletion (deleteWorksheet) is the only structural delete.

If asked for any of these, tell the user it's unsupported and stop.

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Auth

Every shape passes auth as one connection selector, not the secret — a [<resolver>:]<value> string. Every connector accepts zapier:<connection-id> (Zapier-managed auth — routes through Zapier's auth, retries, and governance layer); some also accept one or more direct-token resolvers (naming and count vary per connector) — check this connector's own resolvers rather than assuming. The <resolver>: prefix is optional; a bare value goes to the first resolver that claims it — a UUID-shaped bare value always claims zapier:. Each script declares the connections it needs and the resolvers each accepts. The exact syntax for passing a connection (and how to see this connector's resolver list) differs by shape — see the reference you loaded above.

Checking what's already configured first? Don't dump environment values to do it — env or env | grep <name> prints the value along with the name, leaking a live credential into the transcript if one is set. Check names only (env | cut -d= -f1 | grep -i <name>) or test a known name directly ([ -n "$VAR_NAME" ]).

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No connection yet? Pick one — and follow the reference's own flow to obtain it; never just ask the user for a connection id or token as if they already have one memorized:

Output format

Every script returns a { data, meta } envelope:

• data — the script's result (the shape its outputSchema declares; see the reference you loaded above for how to inspect a script's exact schema in your shape).
• meta.outputDataValidation — what validating data did:
  • { skipped: false, droppedPaths: null } — validated, nothing removed.
  • { skipped: false, droppedPaths: [...], instruction } — validated, but those paths were stripped from data: fields the script returned from the API that the outputSchema doesn't declare. If you need them, re-run with output validation skipped.
  • { skipped: true } — validation was bypassed; data is the raw, unchecked script output.

Reading dropped fields / skipOutputDataValidation. To receive the raw, unvalidated result, opt out of output validation (the exact syntax differs by shape — see the reference you loaded above). Input validation is never skipped.

Trimming the result / filterOutputData. To shrink a large result down to the fields you need, pass a jq expression that post-processes data (again, exact syntax per shape). The jq runs against data only, NOT the { data, meta } envelope, so write it rooted at data (run the script's --help — or your shape's equivalent — to see its output schema). The transformed value replaces data, meta is preserved, and the result is NOT re-validated against the output schema.

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References

Load the matching reference file before working in that area:

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