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screenshot_build_all

Generate localized App Store screenshots for iOS apps: build and install the app, capture 32 languages across 6 key screens, brand them, and sync to fastlane.

Instructions

MANDATORY turnkey screenshot step (UI-test style, all apps): generate a sample image → build+install → localized captions from the catalog → capture 32 languages × 6 rich screens (onboarding/create/ result/gallery/paywall/settings) → brand → sync to fastlane/screenshots. sample_prompt: a sample prompt for what the app produces (fills Result/Gallery with real output).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
udidYes
schemeYes
app_dirYes
localesNo
projectYes
bundle_idYes
sample_promptYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does substantial work: it discloses a multi-stage pipeline, that it builds and installs, the scale (32 languages × 6 screens), and the output location (fastlane/screenshots). It still omits permissions/prereqs (e.g. a booted simulator matching udid) and whether files are overwritten, keeping it short of a 5.

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?

Front-loaded with the purpose and mandatory framing, with the pipeline conveyed efficiently via arrow notation. Dense but each element describes a real stage; only sample_prompt's trailing gloss is slightly tacked-on.

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

Completeness3/5

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

An output schema exists, so return-value explanation is rightly omitted, and the pipeline is well described. But for a complex 7-param tool with zero annotation coverage and zero schema-description coverage, leaving six parameters undocumented is a meaningful completeness gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 7 params, so the description must compensate, but it explains only sample_prompt ('a sample prompt for what the app produces') and indirectly hints at locales via '32 languages'. The other five parameters (app_dir, project, scheme, bundle_id, udid) get no meaning at all, so most semantics are absent.

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?

Uses a specific verb+resource and enumerates the full pipeline (generate sample → build+install → localized captions → capture 32 languages × 6 screens → brand → sync to fastlane/screenshots), so the scope is unmistakable. However, it never explicitly names the sibling component tools (screenshot_capture, screenshot_brand, screenshot_sync) it aggregates, so the agent must infer this is the turnkey aggregate.

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?

'MANDATORY turnkey ... step (UI-test style, all apps)' signals this is the always-use screenshot step, implying when to invoke it. But there is no explicit when-not guidance and no routing to or away from the granular sibling tools, leaving the choice entirely to inference.

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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