Rowfeed Search & Market Data

rowfeed/google-sheets-export

rowfeed--google-sheets-export

Destructive

This tool calls the Actor "rowfeed/google-sheets-export" and retrieves its output results. Actor description: Send Apify datasets, CSV and JSON files to any Google Sheet: share the sheet with our export account, no OAuth. Append or replace, skip rows already in the sheet, new columns added. $0.05 per 1,000 rows.

Input Schema

Name	Required	Description	Default
`mode`	No	Append keeps what is in the tab and adds new rows below. Replace clears the tab first (never when there is nothing to write). Example values: "append"	append
`rows`	No	Or paste rows as a JSON array of objects.
`columns`	No	Optional: export only these columns, in this order. Nested fields are columns with dots (address.city).
`fileUrls`	No	Public links to CSV, JSON or JSON Lines files (up to 20 MB each).
`waitSecs`	No	Max seconds (0–45, default 30) to cap the wait for the Actor run to reach terminal state. For long-running Actors the response returns at the cap with the current run status; follow `nextStep` to poll via get-actor-run. Set to 0 to fire-and-forget.
`sheetName`	No	Tab (worksheet) to write to. Created if it does not exist. Empty = the first tab.
`datasetIds`	No	Datasets to export, e.g. a run's results. In an integration (run another Actor, then this one) use {{resource.defaultDatasetId}}.
`spreadsheet`	No	Link to your Google Sheet. Before the first run: open the sheet, click Share and add the export account (shown in the README and in the run log) as Editor. No Google login or OAuth needed.
`skipDuplicatesBy`	No	E.g. url or email: rows whose value is already in the sheet (or earlier in this export) are skipped and not charged. Ideal for daily runs that add only new results.

Output Schema

Name	Required	Description
`tip`	No	Advisory guidance RAG Web Browser wrote to its key-value store under the reserved "TIP" key
`runId`	Yes	Actor run ID
`stats`	No	Run statistics
`status`	Yes	Run status: READY \| RUNNING \| TIMING-OUT \| TIMED-OUT \| ABORTING \| ABORTED \| SUCCEEDED \| FAILED
`actorId`	Yes	Stable Apify Actor ID from the run record
`summary`	Yes	Past-tense summary of the run state
`exitCode`	No	Actor process exit code; populated for terminal states (especially FAILED)
`nextStep`	Yes	One primary follow-up action with identifiers interpolated
`storages`	Yes	Dataset and key-value store metadata, keyed by alias. "default" is always the primary entry.
`actorName`	No	"username/actor-name"
`startedAt`	No	ISO timestamp when the run started
`finishedAt`	No	ISO timestamp when the run finished (terminal states only)
`statusMessage`	No	Pass-through from Apify run.statusMessage
`apifyConsoleUrl`	No	Personalized Apify Console link to the run; present only for Console sessions

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 7, 2026

addedOutput schema / properties / storages / properties / datasets / properties / default / properties / itemsSchema

Added value: +{
+  "description": "JSON Schema for rows in the dataset at `storages.datasets.default.id` — describes row shape only; the rows themselves are NOT returned inline in this response. Inferred from this Actor's historical successful runs. To fetch actual rows, call `get-dataset-items` with the dataset id and a `fields` projection drawn from this schema.",
+  "properties": {
+    "message": {
+      "type": "string"
+    },
+    "share_with": {
+      "type": "string"
+    },
+    "status": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

Changed1 schema field changedNov 6, 2026

removedOutput schema / properties / storages / properties / datasets / properties / default / properties / itemsSchema

Removed value: -{
-  "description": "JSON Schema for rows in the dataset at `storages.datasets.default.id` — describes row shape only; the rows themselves are NOT returned inline in this response. Inferred from this Actor's historical successful runs. To fetch actual rows, call `get-dataset-items` with the dataset id and a `fields` projection drawn from this schema.",
-  "properties": {
-    "columns": {
-      "type": "number"
-    },
-    "message": {
-      "type": "string"
-    },
-    "mode": {
-      "type": "string"
-    },
-    "rows_written": {
-      "type": "number"
-    },
-    "share_with": {
-      "type": "string"
-    },
-    "sheet": {
-      "type": "string"
-    },
-    "skipped_duplicates": {
-      "type": "number"
-    },
-    "source_errors": {
-      "items": {},
-      "type": "array"
-    },
-    "spreadsheet_url": {
-      "type": "string"
-    },
-    "status": {
-      "type": "string"
-    }
-  },
-  "type": "object"
-}

AddedNov 6, 2026

TDQS

A3.6/5.0

Behavior4/5

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

The annotations already flag destructiveHint=true and openWorldHint=true, but the description adds real context beyond them: the sheet must be shared with the export account as Editor before the first run, no OAuth is required, replace clears the tab first (never when there is nothing to write), skipped duplicate rows are not charged, and pricing is $0.05 per 1,000 rows. Auth prerequisite and cost disclosure are exactly the kind of behavior the agent cannot infer from the schema. It stops short of describing failure modes or what the run log returns, which the output schema partly covers.

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

Conciseness3/5

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

The second sentence is dense and well packed, but the first sentence is boilerplate that restates the tool name and mischaracterizes the operation as output retrieval. The most useful fact for a first-time caller, the mandatory sheet-sharing step, is buried mid-sentence rather than front-loaded.

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?

For a nine-parameter mutation tool with annotations and an output schema, the description covers the critical missing pieces: the Editor-sharing prerequisite, no-OAuth model, destructive replace semantics, dedup cost behavior, and per-row pricing. The waitSecs/get-actor-run polling flow and response shape are adequately handled by the schema and output schema, so their absence from the description is minor.

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 100%, so all nine parameters, including the mode enum, waitSecs polling cap and skipDuplicatesBy semantics, are already documented inline. The description reinforces append vs replace and the duplicate-skipping behavior but adds no syntax, format, or interaction detail beyond what the schema states. Baseline 3 is appropriate when the schema does the heavy lifting.

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 actor description states a specific verb and resource: send Apify datasets, CSV and JSON files to any Google Sheet, with append/replace modes. It is clearly distinguishable from ingestion siblings like get-dataset-items or rowfeed--dataset-merge-dedup. The opening boilerplate ('calls the Actor ... retrieves its output results') is generic and slightly misdescribes the tool as a retrieval wrapper rather than a write-to-Sheets operation.

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?

Usage is implied by the resource pairing (Apify dataset -> Google Sheet) and by the enumerated modes, but there is no explicit 'use this when X, not Y' guidance. Nothing tells the agent when to prefer get-dataset-items or dataset-merge-dedup instead, and no prerequisites beyond the sharing step are given. Adequate but clearly gapped.

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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