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store_compare

Bulk-fetch apps by ID to generate a side-by-side comparison table for competitive research, including hearts, platforms, release dates, and more.

Instructions

Bulk-fetch several apps by id and return a side-by-side comparison table for competitive research.

rows has one entry per found app with: hearts, type, the list of compatible platforms, a has_emery flag, the latest release date, and the .pbw URL. Any ids that don't resolve come back in missing_ids rather than failing the whole call.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "app_ids": "<app_ids>"
    },
    "name": "store_compare"
  }
}

Input Schema

Name	Required	Description	Default
`app_ids`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden and does well: it explains what rows contain and explicitly states the graceful missing-id behavior (returned in missing_ids rather than failing). It doesn't cover authentication or rate limits, but for a read-only bulk fetch the key behaviors are disclosed.

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 compact: a one-sentence purpose, a one-sentence breakdown of rows, and a one-sentence error-handling note. The most important information is front-loaded and every sentence adds value.

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?

Given the tool's modest complexity (one parameter, a comparison-table output), the description fully covers what the agent needs: what ids to pass, what fields to expect, and how missing ids are handled. The output schema could provide further detail on return types, but the description alone is sufficient for correct selection and invocation.

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?

The only parameter, app_ids, has no schema description (0% coverage), so the description must compensate. It does by explaining the tool fetches 'several apps by id' and that unresolved 'ids' appear in missing_ids, making the parameter's meaning and behavior clear beyond the raw schema.

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 opens with a specific action ('Bulk-fetch several apps by id') and output ('side-by-side comparison table'), which clearly distinguishes it from sibling tools like store_search or store_app. The purpose is immediately understandable and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

The phrase 'for competitive research' gives clear usage context, and the tool's batch-by-id nature implies using it when you have specific app identifiers rather than a search query. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full guidance.

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