ios_pcap_status
Report the active native packet capture on an iOS device: whether it is running and how many bytes have been captured so far.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| udid | Yes | iOS device UDID |
Report the active native packet capture on an iOS device: whether it is running and how many bytes have been captured so far.
| Name | Required | Description | Default |
|---|---|---|---|
| udid | Yes | iOS device UDID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. The description discloses it reports status (running + byte count), which is a read-only behavioral trait. However, it doesn't disclose what happens if no active capture exists (returns not-running vs error), or whether this tool affects the capture in any way. The read-only nature is reasonably implied but could be more explicit given no annotations.
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 a single, tightly-worded sentence with zero waste. It front-loads the main purpose (report status) and includes the two key data points (running state and byte count) without padding.
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?
The tool is simple: one parameter, no output schema, read-only status query. The description covers the core behavior (reports running status and byte count). Minor gaps include what happens when no capture is active and the exact output format, but for a simple status-check tool with strong sibling context (pcap_start/pcap_stop), this is largely complete.
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?
Schema coverage is 100% with a single udid parameter described as 'iOS device UDID'. The description doesn't need to add much for a single self-explanatory parameter. The description effectively conveys the tool operates on a specific iOS device, which complements the parameter. Baseline 3 applies, with the 'native packet capture' context adding marginal value, so 4 is slightly generous but reasonable given the parameter's simplicity.
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 clearly states the tool reports active native packet capture status on an iOS device, specifying it reports whether capture is running and bytes captured. This distinguishes it from sibling tools ios_pcap_start and ios_pcap_stop, which clearly initiate and stop capture respectively.
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?
The description implies usage context (after starting a pcap capture via ios_pcap_start, to check its status) but does not explicitly state when to use it vs alternatives. It references 'active' capture, which implies it should be used during an ongoing capture session, but no explicit when/when-not guidance is given.
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.