Take a HIGH-FIDELITY screenshot of an iOS device (slow full-fidelity path). Returns base64 PNG image. For routine per-action screen checks prefer ios_mjpeg_screenshot — it is much faster; reserve ios_screenshot for when you need a crisp, full-fidelity frame. If the device is asleep (black frame) it auto-wakes and retries once. Requires an active iOS automation session (auto-starts if needed).
| Name | Required | Description | Default |
|---|---|---|---|
| udid | Yes | iOS device UDID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses key behaviors: returns a base64 PNG, is slow/full-fidelity, auto-wakes and retries if device is asleep, and requires an active iOS session (auto-starts if needed). This goes beyond basic expectations and helps the agent anticipate behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the core purpose, and each sentence adds value: purpose, output, usage guidance, and behavioral caveats. There is no redundancy or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool without an output schema, the description is complete: it explains the return format, the speed/fidelity tradeoff, the auto-wake retry behavior, and session requirements. It effectively distinguishes from relevant siblings and gives the agent enough to decide when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers the single parameter 'udid' with description 'iOS device UDID', achieving 100% schema coverage. The description adds no additional semantic detail about the parameter, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states precisely what the tool does: 'Take a HIGH-FIDELITY screenshot of an iOS device (slow full-fidelity path).' It clearly identifies the resource (iOS device) and action (screenshot), and differentiates from the sibling tool ios_mjpeg_screenshot by highlighting the fidelity/speed tradeoff.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use this tool vs. the alternative: 'For routine per-action screen checks prefer ios_mjpeg_screenshot — it is much faster; reserve ios_screenshot for when you need a crisp, full-fidelity frame.' Also notes the auto-wake and session auto-start behavior, giving clear context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set contains near-identical duplicate families: web_* and playwright_* expose ~15 pairs of the same desktop-grid-browser operations (web_get_text/playwright_get_text, web_reload/playwright_reload), and screenshot/log/network/mock capabilities each have 5-8 entry points (device_screenshot vs android_mjpeg_screenshot vs ios_screenshot vs ios_fast_screenshot vs web_screenshot vs webpage_screenshot vs session_screenshot). Many individual descriptions carefully draw boundaries (devtools vs traffic, HID vs session), but an agent cannot reliably distinguish web_* from playwright_*, and ios_screenshot/ios_fast_screenshot/ios_mjpeg_screenshot blur together.
The prefix scheme is broken: Android functionality is split arbitrarily between android_* and device_* (device_screenshot vs android_mjpeg_screenshot), the desktop browser gets two parallel prefixes (web_* and playwright_*), and verbs vary across equivalents (device_navigate_url vs web_navigate vs ios_safari_navigate). session_* uses bare verbs (session_url, session_back), and the same concept gets different names (ios_clipboard_get_hid vs ios_get_pasteboard; device_screen vs ios_orientation).
333 tools is an extreme count by any measure — far beyond the 50+ threshold — and much of the bulk is duplicative (the web_*/playwright_* pairs alone double ~15 slots) or out-of-scope for a device-control server (TestRail, Jira, AzDO, agent memory, secret variables, feedback). Even granting that remote device control + test automation is a broad domain, this surface will devastate agent context budgets and is impossible to navigate coherently.
The core device-control and test-automation domain is remarkably thorough: Android and iOS each have full interaction, app-lifecycle, file, network/proxy, performance, crash, accessibility, recording, and replay coverage, with CRUD lifecycles for flows, suites, app uploads, TestRail cases, and visual-review baselines. Minor gaps exist at the margins — Jira/AzDO lack update/transition/comment operations, and iOS cannot open/close tabs — but the central workflows have no dead ends.
