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vom-core

storelift-mcp

export_data

Preview and download an app's keyword ranks, rank history, or reviews as CSV/JSON for analysis.

Instructions

PREVIEW ONLY: export one app's data as a CSV or JSON file — keywords (the latest run's rank, demand and search depth per tracked keyword), history (daily rank per keyword between from and to) or reviews (rating, title, text, author, version between from and to). The preview shows the row count, columns and date range; confirm_action returns a download link that stays valid for 7 days and always serves the same table. 20 credits on confirm. Pro plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNohistory/reviews: last day YYYY-MM-DD (default today)
fromNohistory/reviews: first day YYYY-MM-DD (defaults to the plan's API window / the last 90 days)
appIdYesapp id from list_apps
formatNofile format (default csv)
countryNotwo-letter storefront (defaults to the app's first country)
datasetYes
platformNostore (default ios)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description discloses preview contents, the 7-day link lifetime, that the link always serves the same table (stable snapshot), credit cost, and the plan requirement. These are real operational traits an agent cannot infer from the annotations.

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?

Lead-in is front-loaded with the critical 'PREVIEW ONLY' constraint, and the dataset enumeration is compact. The single dense paragraph is slightly information-heavy but every clause (datasets, preview output, confirm flow, cost, plan) carries weight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description covers what the preview returns (row count, columns, date range) and what confirm_action yields, plus cost and plan gating. Nothing needed to invoke or sequence the tool correctly is missing.

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 coverage is 86% so the schema already documents most parameters; the description adds value by defining what each dataset enum value yields and clarifying that from/to bound the history and reviews datasets. It leaves country, platform and format purely to the schema, so it is above the baseline but not exhaustive.

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 names a specific verb (export) and resource (one app's data) and enumerates the three datasets with the exact fields each returns. An agent can distinguish this from siblings like get_history or get_reviews because it is framed as a bulk file export with a preview/confirm flow.

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?

It explicitly states this is PREVIEW ONLY and names confirm_action as the tool that produces the download link, giving the correct two-step ordering. Cost (20 credits on confirm) and plan gating (Pro plan) tell the agent when the call will fail.

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